745 results on '"ELECTRIC power consumption"'
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2. NILM for Commercial Buildings: Deep Neural Networks Tackling Nonlinear and Multi-Phase Loads.
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Kulathilaka, M. J. S., Saravanan, S., Kumarasiri, H. D. H. P., Logeeshan, V., Kumarawadu, S., and Wanigasekara, Chathura
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ARTIFICIAL neural networks , *ENERGY conservation , *ELECTRIC power consumption , *ENERGY consumption , *ENERGY management , *COMMERCIAL buildings - Abstract
As energy demand and electricity costs continue to rise, consumers are increasingly adopting energy-efficient practices and appliances, underscoring the need for detailed metering options like appliance-level load monitoring. Non-intrusive load monitoring (NILM) is particularly favored for its minimal hardware requirements and enhanced customer experience, especially in residential settings. However, commercial power systems present significant challenges due to greater load diversity and imbalance. To address these challenges, we introduce a novel neural network architecture that combines sequence-to-sequence, WaveNet, and ensembling techniques to identify and classify single-phase and three-phase loads using appliance power signatures in commercial power systems. Our approach, validated over four months, achieved an overall accuracy exceeding 93% for nine devices, including six single-phase and four three-phase loads. The study also highlights the importance of incorporating nonlinear loads, such as two different inverter-type air conditioners, within NILM frameworks to ensure accurate energy monitoring. Additionally, we developed a web-based NILM energy dashboard application that enables users to monitor and evaluate load performance, recognize usage patterns, and receive real-time alerts for potential faults. Our findings demonstrate the significant potential of our approach to enhance energy management and conservation efforts in commercial buildings with diverse and complex load profiles, contributing to more efficient energy use and addressing climate change challenges. [ABSTRACT FROM AUTHOR]
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- 2024
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3. Long Short-Term Memory Autoencoder and Extreme Gradient Boosting-Based Factory Energy Management Framework for Power Consumption Forecasting.
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Moon, Yeeun, Lee, Younjeong, Hwang, Yejin, and Jeong, Jongpil
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CLEAN energy , *ENERGY management , *FORGING (Manufacturing process) , *ELECTRIC power consumption , *REAL-time computing - Abstract
Electricity consumption prediction is crucial for the operation, strategic planning, and maintenance of power grid infrastructure. The effective management of power systems depends on accurately predicting electricity usage patterns and intensity. This study aims to enhance the operational efficiency of power systems and minimize environmental impact by predicting mid to long-term electricity consumption in industrial facilities, particularly in forging processes, and detecting anomalies in energy consumption. We propose an ensemble model combining Extreme Gradient Boosting (XGBoost) and a Long Short-Term Memory Autoencoder (LSTM-AE) to accurately forecast power consumption. This approach leverages the strengths of both models to improve prediction accuracy and responsiveness. The dataset includes power consumption data from forging processes in manufacturing plants, as well as system load and System Marginal Price data. During data preprocessing, Expectation Maximization Principal Component Analysis was applied to address missing values and select significant features, optimizing the model. The proposed method achieved a Mean Absolute Error of 0.020, a Mean Squared Error of 0.021, a Coefficient of Determination of 0.99, and a Symmetric Mean Absolute Percentage Error of 4.24, highlighting its superior predictive performance and low relative error. These findings underscore the model's reliability and accuracy for integration into Energy Management Systems for real-time data processing and mid to long-term energy planning, facilitating sustainable energy use and informed decision making in industrial settings. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Short-Term Energy Forecasting Using an Ensemble Deep Learning Approach
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Prasad, P. Yogendra, Ramu, M., Yasaswini, Annavarapu, Gowthami, Mallela, Harika, Putta Sai, Abhishek, Chettipalli, Fournier-Viger, Philippe, Series Editor, Madhavi, K. Reddy, editor, Subba Rao, P., editor, Avanija, J., editor, Manikyamba, I. Lakshmi, editor, and Unhelkar, Bhuvan, editor
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- 2024
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5. Modeling energy management of an energy hub with hybrid energy storage systems for a smart island considering water–electricity nexus.
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Sadeghi, Saleh, Ahmadian, Ali, Diabat, Ali, and Elkamel, Ali
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ENERGY storage , *NETWORK hubs , *MIXED integer linear programming , *ENERGY management , *ELECTRIC power consumption , *RENEWABLE energy transition (Government policy) , *CONVOLUTIONAL neural networks - Abstract
Energy hubs (EHs) represent a pivotal paradigm in achieving optimal resource utilization across various energy domains. This paper presents an advanced framework for the optimal management of a smart island, leveraging the synergies within the water-electricity nexus. By integrating diverse resources, including electricity, water, heat, and hydrogen, the proposed EH model aims to meet the multifaceted demands of consumers on the island. To enhance operational efficiency, this study delves into the nuanced impacts of key EH components, elucidating their roles in meeting demand profiles and minimizing operational costs. Formulated as a mixed integer linear programming (MILP) model, the EH optimization problem is addressed using the GAMS optimization tool. The overarching objective is to fulfill consumer demand while concurrently optimizing resource utilization, considering factors such as storage degradation costs and emissions from fossil-fuel-based units. In addition to strategic optimization, this study pioneers a novel approach to stochastic parameter forecasting, integrating convolutional neural networks (CNNs) and long-short-term memory networks (LSTMs). By harnessing the capabilities of these advanced forecasting techniques, the EH model can anticipate dynamic changes in demand patterns with heightened accuracy and precision. The empirical results underscore the transformative potential of the proposed EH framework, showcasing significant reductions—up to 30%—in emission costs. Moreover, the study underscores the pivotal role of EHs as enablers for scaling up renewable energy penetration, offering a robust foundation for sustainable energy transitions in island communities and beyond. Additionally, implementing a load-shifting demand response program can lower total costs by approximately $257 per day, offering significant savings for EHs over extended periods. [Display omitted] • Modeling a mixed integer linear programming for energy management of an energy hub. • Comprehensive study on hydrogen and electric storage systems. • Uncertainty modeling using convolutional and long short-term memory networks. • Modeling water–electricity nexus to meet water and electricity demand. [ABSTRACT FROM AUTHOR]
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- 2024
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6. CLEMD, a circuit-level electrical measurements dataset for electrical energy management.
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Al-Khadher, Omar, Mukhtaruddin, Azharudin, Hashim, Fakroul Ridzuan, Azizan, Muhammad Mokhzaini, Mamat, Hussin, and Aqlan, Ahmed
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ELECTRICAL energy ,ENERGY consumption ,ENERGY management ,COMMERCIAL building energy consumption ,ELECTRICAL harmonics ,ELECTRIC power consumption ,REACTIVE power ,POWER factor measurement - Abstract
Enhancing energy efficiency in commercial buildings is crucial for reducing energy consumption. Achieving this goal requires careful monitoring and analysis of the energy usage patterns exhibited by different devices. Nonetheless, gathering data from individual appliances in commercial buildings presents difficulties due to the large number of appliances, complex installations, and costs. This paper presents the Circuits-Level Electrical Measurements Dataset (CLEMD). The measurement was conducted at the main switchboard to a set of distribution boards instead of measuring at the individual loads. The data is gathered from an institutional setting. It consists of 42 records of vital electrical parameters including voltage, current, frequency, real power, reactive power, apparent power, power factor, and odd harmonics for electrical currents. The device deployed in the measurement were industry-grade and had a high sampling rate of 200 kHz. The measurements were done over a 40-day period, from September 16 2023 to October 25 2023. CLEMD is the first Malaysian public dataset on circuit-level electricity consumption and offers analysis opportunities in different research areas such as electricity load disaggregation at circuit level, circuit identification, load profile forecasting, and pattern recognition. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Quantum support vector machine for forecasting house energy consumption: a comparative study with deep learning models.
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K, Karan Kumar, Nutakki, Mounica, Koduru, Suprabhath, and Mandava, Srihari
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DEEP learning ,ENERGY consumption forecasting ,SUPPORT vector machines ,ENERGY management ,ELECTRIC power consumption ,QUANTUM computing - Abstract
The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model's exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Standby Power Reduction of Home Appliance by the i-HEMS System Using Supervised Learning Techniques.
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Park, Beungyong, Kwon, Suh-hyun, and Oh, Byoungchull
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HOUSEHOLD appliances , *ENERGY management , *WASHING machines , *ELECTRIC power consumption , *SUPERVISED learning , *SOLAR energy , *HOME computer networks - Abstract
Electricity consumption in homes is on the rise due to the increasing prevalence of home appliances and longer hours spent indoors. Home energy management systems (HEMSs) are emerging as a solution to reduce electricity consumption and efficiently manage power usage at home. In the past, numerous studies have been conducted on the management of electricity production and consumption through solar power. However, there are limited human-centered studies focusing on the user's lifestyle. In this study, we propose an Intelligent Home Energy Management System (i-HEMS) and evaluate its energy-saving effectiveness through a demonstration in a standard house in Korea. The system utilizes an IoT environment, PID sensing, and behavioral pattern algorithms. We developed algorithms based on power usage monitoring data of home appliances and human body detection. These algorithms are used as the primary scheduling algorithm and a secondary algorithm for backup purposes. We explored the deep connection between power usage, environmental sensor data, and input schedule data based on Long Short-Term Memory network (LSTM) and developed an occupancy prediction algorithm. We analyzed the use of common home appliances (TV, computer, water purifier, microwave, washing machine, etc.) in a standard house and the power consumption reduction by the i-HEMS system. Through a total of six days of empirical experiments, before implementing i-HEMS, home appliances consumed 13,062 Wh. With i-HEMS, the total consumption was reduced to 10,434 Wh (a 20% reduction), with 9060 Wh attributed to home appliances and 1374 Wh to i-HEMS operation. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Forecasting Electricity Consumption for Accurate Energy Management in Commercial Buildings With Deep Learning Models to Facilitate Demand Response Programs.
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Erten, Mustafa Yasin and İnanç, Nihat
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COMMERCIAL building energy consumption , *COMMERCIAL buildings , *ENERGY consumption , *ELECTRIC power consumption , *DEEP learning , *ENERGY management , *MACHINE learning , *LOAD management (Electric power) - Abstract
In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Energy Management System for the Campus Microgrid Using an Internet of Things as a Service (IoTaaS) with Day-ahead Forecasting.
- Author
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Zhakiyev, Nurkhat, Satan, Aidos, Akhmetkanova, Gulnar, Medeshova, Aigul, Omirgaliyev, Ruslan, and Bracco, Stefano
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ENERGY consumption forecasting ,ENERGY management ,ELECTRIC power consumption ,INTELLIGENT control systems ,UBIQUITOUS computing ,MICROGRIDS - Abstract
In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Selection of Renewable Energy Sources for Modular and Mobile "Green Classroom" Facilities.
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Węgiel, Tomasz, Borkowski, Dariusz, Blazy, Rafał, Ciepiela, Agnieszka, Łysień, Mariusz, Dudek, Jakub, Błachut, Jakub, Hrehorowicz-Gaber, Hanna, and Hrehorowicz-Nowak, Alicja
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RENEWABLE energy sources , *HEAT pumps , *ENERGY consumption , *SCHOOL facilities , *ELECTRIC power consumption , *ENERGY management - Abstract
This article aims to demonstrate the technical capabilities and effectiveness of an energy production and management system for school facilities using a modular solution. The system is assumed to generate electricity from renewable sources, such as wind or sun. The potential of renewable energy sources in Cracow, Poland, was assessed, with a focus on solar energy (photovoltaic panels, PV). Taking into account the installation of heating and other equipment, an analysis of the facility's electricity demand was carried out. The study recommended the use of a heat pump system to heat and cool the facility. Renewable energy sources will meet 81% of the facility's projected annual demand, according to the study. An analysis of the energy consumption and production profiles shows that almost 69% of the energy produced by the PV panels is consumed on site. Of the remaining energy, 31% is fed back into the grid and sold to the grid operator or used by other facilities within the shared settlement. The overall balance results in a small electricity deficit that must be covered by the grid. If suitable sites are available, the facilities under study could consider installing a wind turbine as a potential supplement to the energy deficit. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Electricity Load Demand Prediction for Microgrid Energy Management System Using Hybrid Adaptive Barnacle‐Mating Optimizer with Artificial Neural Network Algorithm.
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Indira, Gomathinayagam, Bhavani, Munusamy, Brinda, Rajamony, and Zahira, Rahiman
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ARTIFICIAL neural networks ,DEMAND forecasting ,ELECTRIC power consumption ,ENERGY management ,STANDARD deviations ,MICROGRIDS ,SMART devices - Abstract
In recent years, one of the primary causes of high electricity consumption is the growing global population and the availability of power‐demanding smart devices. As a result, accurate load forecasting tools are required for effective energy conservation in microgrids. Various simulation tools and artificial intelligence (AI)‐based methods have been used to make the best forecasts of electricity demand. Furthermore, conventional systems are static and based solely on historical data. To address this practical need, this work aims to create a machine learning (ML) model for short‐term load forecasting based on feature selection and parameter optimization. This research proposes a hybrid short‐term load demand prediction approach that combines an adaptive barnacle‐mating optimizer (ABMO) and an artificial neural network (ANN). When compared to the regression tree (RT), support vector machine (SVM), ANN, and particle swarm optimization‐based ANN (PSO‐ANN) algorithms, the proposed ABMO‐ANN algorithm reduces mean absolute percentage errors by 67.69%, 64.58%, 59.18%, and 42.02%, respectively. Compared to conventional RT, SVM, ANN, and PSO‐ANN algorithm, the hybrid ABMO with ANN outperforms them based on mean absolute percentage error, root mean square error, correlation coefficient (R2)$\left(\right. R^{2} \left.\right)$, symmetric mean absolute percentage error, and agreement index (d). [ABSTRACT FROM AUTHOR]
- Published
- 2024
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13. Management smart building with photovoltaic using (ANN) to increase energy efficiency.
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Jalal, Bareq Musaab and Al Rubayi, Rashid H.
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ENERGY consumption , *BUILDING-integrated photovoltaic systems , *DIESEL electric power-plants , *ELECTRIC power , *INTELLIGENT buildings , *ENERGY management , *ELECTRIC power consumption , *ELECTRONIC paper - Abstract
the issue of rising electricity demand in the building sector is one of the major challenges. Therefore, the Building Management System (BMS) inside the building. Since the PV, national grid, and diesel generators supplies building electrical power, so the Energy Management System (EMS) technologies are necessary to study the priority of PV to supply building power to save energy. This paper proposes a Smart Building Management System (SBMS) used with Heating, Ventilation, and Air Conditioning (HVAC) in addition lighting systems. HVAC is controlled with occupancy sensors, temperature control, and load control. Lighting systems are controlled through occupancy sensors and daylight sensors to make them operate to minimize electricity consumption. Using Artificial Neural Network (ANN) control of the EMS in this work, SBMS and EMS are created and simulated using two case studies to supply public buildings. The first situation is SBMS without EMS and the second situation is SBMS with EMS. Finally, the results showed the impact of SBMS on the reduction of energy consumption for HVAC systems and lighting (20% on summer day and 11% on winter day), as the energy consumption was reduced for both HVAC and lighting by about (54% on a summer day and 44% on a winter day) because we added the scenario of case two to the one scenario case in a public building. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Stochastic programming approaches for an energy-aware lot-sizing and sequencing problem with incentive.
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Perraudat, Antoine, Dauzère-Pérès, Stéphane, and Mason, Scott Jennings
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INDUSTRIAL management ,ELECTRIC power consumption ,ENERGY management ,STOCHASTIC programming ,ELECTRIC utilities ,ELECTRICITY pricing - Abstract
Motivated by real challenges on energy management faced by industrial firms, we propose a novel way to reduce production costs by including the pricing of electricity in a multi-product lot-sizing problem. In incentive-based programs, when electric utilities face power consumption peaks, they request electricity-consuming firms to curtail their electric load, rewarding the industrial firms with incentives if they comply with the curtailment requests. Otherwise, industrial firms must pay financial penalties for an excessive electricity consumption. A two-stage stochastic formulation is presented to cover the case where a manufacturer wants to satisfy any curtailment request. A chance-constrained formulation is also proposed, and its relevance in practice is discussed. Finally, computational studies are conducted to compare mathematical models and highlight critical parameters and show potential savings when subscribing incentive-based programs. We show that the setup cost ratio, the capacity utilisation rate, the number of products and the timing of curtailment requests are critical parameters for manufacturers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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15. Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model.
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La Tona, G., Luna, M., and Di Piazza, M.C.
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ELECTRIC power consumption forecasting , *ENERGY management , *ELECTRIC power consumption , *ENERGY consumption , *ELECTRICAL load , *DEMAND forecasting , *STATISTICAL errors - Abstract
Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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16. DESIGN OF PROACTIVE MANAGEMENT SYSTEM FOR RESIDENTIAL BUILDINGS BY USING SMART EQUIPMENT.
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Savytskyi, Mykola, Shekhorkina, Svitlana, Bordun, Maryna, Babenko, Maryna, Tsyhankova, Svitlana, Spyrydonenkov, Vitalii, Savytskyi, Oleksandr, and Rabenseifer, Roman
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ELECTRIC power consumption ,INTELLIGENT buildings ,ENERGY consumption ,SMART meters ,ATMOSPHERIC temperature ,DWELLINGS ,HUMIDITY ,APARTMENTS - Abstract
This study's object is an energy efficiency of residential sector. The work is aimed at solving the task to improve the energy efficiency of the housing sector by devising technical solutions for monitoring and managing energy consumption and microclimate parameters of buildings. The proposed proactive management system for residential buildings consists of multi-sensors measuring CO
2 , temperature and humidity, smart meters of heat and electricity consumption, and smart plugs. The equipment is combined into single system through an integration controller with remote user access through an interactive web interface. A feature of the technical solution is the ability to collect, process, visualize, and archive data on the consumption of energy, as well as on the key parameters of the microclimate of residential premises. The advantages of the system are its flexibility due to the possibility of integrating additional devices during operation, as well as the use of standard communication protocols, which enables the interchangeability of component elements. The implementation and testing were carried out under the conditions of a real pilot site. The use of the system in practice confirmed the efficiency and stability of the operation, making it possible to obtain data on the parameters of energy consumption and microclimate and devising recommendations for reducing energy consumption at the pilot site. It was established that the microclimate meets the requirements of the standards (air temperature is about 22 °С while relative humidity does not exceed 60 %). Decrease in energy consumption can be achieved by reducing the temperature of the heat carrier in the absence of residents, as well as by considering the influence of weather conditions. During periods of residents activity, an excess of the permissible level of CO2 was recorded, therefore, automatic ventilation systems should be provided in the apartments. [ABSTRACT FROM AUTHOR]- Published
- 2024
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17. Daily average load demand forecasting using LSTM model based on historical load trends.
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Bareth, Rashmi, Yadav, Anamika, Gupta, Shubhrata, and Pazoki, Mohammad
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DEMAND forecasting , *MACHINE learning , *MICROGRIDS , *ELECTRIC power distribution grids , *ELECTRIC power consumption , *ENERGY management - Abstract
Load demand forecasting is very important for the management, designing and analysis of an electrical grid system. Load forecasting has progressively become a crucial component of the energy management system with the growth of the smart micro grid. This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends. The main objective of this research work is estimating monthly electricity demand of an Indian state Chhattisgarh, in terms of per day average load demand using a machine learning model—Long Short‐Term Memory (LSTM). This framework considers average of each day load demand for every month of years 2018–2022 and forecasted per day average load demand for each month of the year 2023. Furthermore, the predicting accuracy is evaluated for training and testing phase, in terms of error metrics like Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The MAPE values under the training and testing phase are in the range of 0.010%–0.652% and 0.378%–10.54%, respectively. A comparative study of LSTM model with Artificial Neural Network (ANN) model indicates the proposed LSTM model is more accurate and can be applied for real time load demand forecasting. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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18. Efficient Techniques for Residential Appliances Scheduling in Smart Homes for Energy Management Using Multiple Knapsack Problem.
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Shewale, Amit, Mokhade, Anil, Lipare, Amruta, and Bokde, Neeraj Dhanraj
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HOME energy use , *SMART homes , *BACKPACKS , *ENERGY management , *ENERGY infrastructure , *HOME wireless technology , *ELECTRIC power consumption , *KNAPSACK problems - Abstract
The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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19. Modelling and Optimization of Residential Electricity Load under Stochastic Demand.
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Mubiru, Kizito Paul and Ssempijja, Maureen Nalubowa
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ELECTRIC power consumption ,MATHEMATICAL optimization ,STOCHASTIC models ,ENERGY management ,DECISION making - Abstract
The paper considers a modelling framework for a set of households in residential areas using electricity as a form of energy for domestic consumption. Considering the demand and availability of units for electricity consumption, optimal decisions for electricity load allocation are paramount to sustain energy management. We formulate this problem as a stochastic decision-making process model where electricity demand is characterized by Markovian demand. The demand and supply phenomena govern the loading and operational framework, where shortage costs are realized when demand exceeds supply. Empirical data for electricity consumption was collected from fifty households in two residential areas within the suburbs of Kampala in Uganda. Data collection was made at hourly intervals over a period of four months. The major problem focussed on determining an optimal electricity loading decision to minimize consumption costs as demand changes from one state to another. Considering a multi-period planning horizon, an optimal decision was determined for loading or not loading additional electricity units using the Markov decision process approach. The model was tested, and the results demonstrated the existence of optimal state-dependent decision and consumption costs considering the case study used in this study. The proposed model can be cost-effective for managers in the electricity industry. Improved efficiency and utilization of resources for electricity distribution systems to residential areas were realized, with subsequently enhanced service reliability to essential energy market customers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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20. A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities.
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Matos, Miguel, Almeida, João, Gonçalves, Pedro, Baldo, Fabiano, Braz, Fernando José, and Bartolomeu, Paulo C.
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ELECTRIC power consumption , *ENERGY consumption forecasting , *ENERGY management , *RENEWABLE energy sources , *ENERGY industries , *MACHINE learning , *DEMAND forecasting , *ENVIRONMENTAL impact analysis - Abstract
The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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21. Demand response-based cost mitigation strategy in renewable energy connected microgrid using intelligent energy management system.
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Vaikund, Harini and Srivani, S. G.
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MICROGRIDS , *ENERGY management , *RENEWABLE energy sources , *POWER resources , *ELECTRIC power consumption , *INTELLIGENT control systems - Abstract
A microgrid was a mixed device of distributed energy resources that contain renewable energy resources, power storage devices and loads and has the capacity to operate locally in a single controllable entity. However, rising electricity costs and rising consumer electricity demand were major problems in worldwide. An energy management system (EMS) was integrated into the system to address these problems. Yet, the managing between load and source and economic problems were a challenging task for the power system industry. Several approaches were developed to manage the EMS to overcome these issues, but it consumes too much time in energy reporting and difficult to solve the energy challenges. So, a novel energy management system was proposed to manage the power flows to reduce the electricity cost. A standard microgrid was designed like IEEE 6 bus system as per the guidelines of IEEE Standard 1547-2018. PV, grid, and battery were chosen for sources, and in the load, home uses were taken. According to the behaviour of individual person and the accompanying appliances activation power requirement, an actual-time standard dataset was constructed. Using this dataset, the intelligent controller was built to anticipate when the sources will be turned ON and OFF. EMS forecasts the load demand and checks the trained value of an intelligent model to produce a command signal of source's CB. The suggested intelligent-based EMS system performance was analysed at both islanded and grid disconnected mode. The proposed model provides 97% accuracy, 0.059% FPR, and 99.8% specificity. The results show that the proposed intelligent controller provides better prediction performance in both conditions and is therefore more suitable for real-time estimation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station.
- Author
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Wu, Kuo-Yang, Tai, Tzu-Ching, Li, Bo-Hong, and Kuo, Cheng-Chien
- Subjects
ELECTRIC vehicles ,ENERGY management ,CORPORATE profits ,INFRASTRUCTURE (Economics) ,ELECTRIC vehicle charging stations ,ENERGY storage ,ELECTRIC power consumption - Abstract
Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners' net income by 1.25 times compared to merely installing charging infrastructure. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
23. Evaluation of Electricity EPI through Preliminary Audit of Electric Energy at CHARUSAT Campus.
- Author
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Patel, Nilaykumar A., Patel, Khush N., and Patel, Keya N.
- Subjects
ELECTRIC power consumption ,ENERGY auditing ,ENERGY consumption ,ENERGY conservation ,ELECTRICITY ,ENERGY management ,CONSUMPTION (Economics) - Abstract
Electrical energy consumption profiles provide a thorough picture of how energy is consumed across different energy users and at different intensities.. Electricity-consuming equipment may encompass a diverse range of sorts that are necessary for the execution of educational activities within a university. The process of conducting an electrical energy audit involves adhering to established standards for electrical energy audits. The determination of electrical energy consumption profiles in university academic operations has been accomplished by the analysis of previous energy usage data and measuring of electrical consumption quantities. The implementation of energy conservation measures significantly influenced the management of the Energy Performance Index (EPI) and electric power consumption profile at the CHARUSAT campus from 2018 to 2022. The evaluation results demonstrate that the Energy Performance Index (EPI) of the CHARUSAT Campus power remains within the criteria for electric energy conservation and is categorized as highly efficient, as per the norms established by the Bureau of Energy Efficiency in India. [ABSTRACT FROM AUTHOR]
- Published
- 2024
24. Seasonal Analysis and Capacity Planning of Solar Energy Demand-to-Supply Management: Case Study of a Logistics Distribution Center.
- Author
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Takada, Akihiko, Ijuin, Hiromasa, Matsui, Masayuki, and Yamada, Tetsuo
- Subjects
- *
BATTERY storage plants , *CAPACITY requirements planning , *ENERGY management , *ENERGY harvesting , *WAREHOUSES , *SEASONS , *STORAGE batteries , *ELECTRIC power consumption , *SOLAR energy - Abstract
In recent years, global warming and environmental problems have become more serious due to greenhouse gas (GHG) emissions. Harvesting solar energy for production and logistic activities in supply chains, including factories and distribution centers, has been promoted as an effective means to reduce GHG emissions. However, it is difficult to balance the supply and demand of solar energy, owing to its intermittent nature, i.e., the output depends on the daylight and season. Moreover, the use of large-capacity solar power generation systems and batteries incurs higher installation costs. In order to maintain low costs, demand-to-supply management of solar energy, based on appropriate seasonal analysis of power generation and consumption and the capacity planning for power generation and the storage battery, is necessary. In this study, the on-demand cumulative control method is applied to actual power consumption data and solar power generation data estimated at a distribution center. Moreover, the monthly, seasonal, and temporal characteristics of power generation and consumption at the distribution center are analyzed. Additionally, the total amount of power purchased is investigated for solar energy demand-to-supply management. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Harvesting sustainability: vertical agricultural greenhouses powered by renewable energy technologies.
- Author
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Lachheb, Aymen, Skouri, Safa, Ayed, Rabeb, El Amine Bekkouche, Sidi Mohammed, and Bouadila, Salwa
- Subjects
- *
RENEWABLE energy sources , *SUSTAINABLE agriculture , *AGRICULTURAL technology , *ENERGY management , *POWER resources , *ELECTRIC batteries , *AIR conditioning efficiency , *ELECTRIC power consumption - Abstract
This study investigates the practicality of integrating renewable energy systems to meet the electricity needs of a self-contained hydroponic greenhouse that functions as an environmentally friendly facility with reliable air conditioning and electrical infrastructure. The first step is to assess the greenhouse’s annual electricity consumption and design a renewable energy system that can sustain this year-round. The results show that daily electricity consumption is between 0.75 and 2 kWh, with thermal air conditioning accounting for over 50% of the total consumption. To address this issue, a simulation model is developed to evaluate the performance of a hybrid photovoltaic/wind system connected to an electric storage battery and to optimize its integration into the greenhouse. This causes the battery’s voltage to generally increase during charging (to 350.5 V) and its state of charge to change non-linearly depending on the battery’s chemical makeup. An Energy Management System (EMS) is then developed for the Hybrid Renewable Energy System (HRES), which works in two modes to ensure the energy balance for uninterrupted power supply. An economic and environmental analysis of three scenarios is developed and concludes that the systems are effective and sustainable, with an estimated payback period of approximately 6 to 8 years. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. An automated energy management framework for smart homes.
- Author
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Kanso, Houssam, Noureddine, Adel, and Exposito, Ernesto
- Subjects
SMART homes ,ENERGY management ,ELECTRIC utilities ,REWARD (Psychology) ,ELECTRIC power consumption - Abstract
Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. Cost-Performance Optimization of a Renewable Energy Resources Based Multimicrogrid System.
- Author
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Othman, Adel Ridha, Yusupov, Ziyodulla, and Guneser, Muhammet Tahir
- Subjects
RENEWABLE energy sources ,ENERGY consumption ,PARTICLE swarm optimization ,ELECTRIC power consumption ,ENERGY management ,MICROGRIDS ,WIND power - Abstract
The growing energy demand, the increase in its price, and the increasing pollution worldwide are considered large problems that need unusual solutions. To overcome these problems, it is necessary to search for innovative solutions using renewable resources and energy management of the energy systems. Energy management means reducing the operating, maintenance, and generation costs of the system and enhancing system performance with methods such as power loss reduction and stability enhancement and alleviating the harmful emissions to the environment. Thus, the energy management of a micro-grid has become one of the most vital aspects of the power or energy system all over the world. The objective of this paper is to optimize a multiobjective problem of a renewable energy resources (RER) based multimicrogid (MMG) system considering the output variation of the photovoltaic (PV) and wind Turbine (WT) as renewable energy resources and variation of the load demand and electricity prices. In this paper three microgrids (MGs) are connected with IEEE 33-bus distribution system each MG consists of a PV and WT. A three objective functions are modeled for this system to optimize the total annual cost, the voltage deviation and the voltage stability index (cost - performance multiobjective function). The optimization problem is solved with particle swarm optimization (PSO) technique for the system with and without RER. A comparison is carried out with two other optimization techniques, mountain gazelle optimization (MGO) and gorilla troop optimization (GTO). The results of the simulation show that the system cost is considerably reduced and the performance optimized. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. Smart EnergyManagement System UsingMachine Learning.
- Author
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Akram, Ali Sheraz, SagheerAbbas, Khan, Muhammad Adnan, Atha, Atifa, Ghazal, TaherM., and Hamadi, Hussam Al
- Subjects
ENERGY management ,ENERGY consumption ,ELECTRIC power consumption ,RENEWABLE energy sources ,CARBON emissions ,ENERGY industries - Abstract
Energy management is an inspiring domain in developing of renewable energy sources. However, the growth of decentralized energy production is revealing an increased complexity for power grid managers, inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand. The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization, minimize energy costs without affecting production, and minimize environmental effects. Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings, which necessitates energy optimization and increased user comfort. To address the issue of energymanagement,many researchers have developed various frameworks; while the objective of each framework was to sustain a balance between user comfort and energy consumption, this problem hasn't been fully solved because of how difficult it is to solve it. An inclusive and Intelligent Energy Management System (IEMS) aims to provide overall energy efficiency regarding increased power generation, increase flexibility, increase renewable generation systems, improve energy consumption, reduce carbon dioxide emissions, improve stability, and reduce energy costs. Machine Learning (ML) is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy (IoE) network. The IoE network is playing a vital role in the energy sector for collecting effective data and usage, resulting in smart resource management. In this research work, an IEMS is proposed for Smart Cities (SC) using the ML technique to better resolve the energy management problem. The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy, and 7.89% miss-rate. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Power load profiles and subsequent analysis for combined energy sources.
- Author
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Baeva, Silvia and Hinova, Ivelina
- Subjects
- *
DATA mining , *ENERGY management , *BIG data , *ELECTRIC power consumption , *STATISTICS , *CITIES & towns - Abstract
The generation of timely expanded load profiles for individual municipalities in the Republic of Bulgaria for the consumption of gas and electricity is of great importance in the optimal management of energy sources in the country. Power-to-Gas (PtG) technology could be used to convert electricity into hydrogen or methane in times of negative residual load. Such load profiles of some regions in the Republic of Bulgaria are made in the present study. There are used the big data and data mining for statistical follow-up analysis for combined energy sources. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
30. Sizing of a solar and hydrogen-based integrated energy system of a stand-alone house in Izmir.
- Author
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Soyturk, Gamze, Kizilkan, Onder, Ezan, Mehmet Akif, and Colpan, C. Ozgur
- Subjects
- *
HYBRID systems , *SOLAR cells , *BATTERY storage plants , *HYDROGEN as fuel , *SOLAR houses , *DIESEL electric power-plants , *ELECTRIC power consumption , *SOLAR radiation , *HYDROGEN production - Abstract
The current work presents the design and modeling of a solar and hydrogen energy-based integrated energy system that provides the electricity demand of a stand-alone house located in Izmir, Türkiye. This system is mainly comprised of photovoltaic (PV) cells, battery banks, a PEM electrolyzer (PEM-El), a hydrogen (H 2) compressor, and a pressurized hydrogen tank. Electricity produced from PV cells was either used in the PEM-El for hydrogen production or stored in the batteries to meet the required energy during low solar radiation or night time. The H 2 produced by the electrolyzer was pressurized with a compressor and stored in pressure tanks at a pressure of 350 bar. A zero-dimensional (0-D) mathematical approach was applied for the system component modeling. An energy management strategy was incorporated into the model to investigate the dynamic behavior of the integrated system. Parametric studies were then conducted for different numbers of PV and batteries. It was found that the amount of hydrogen produced is higher in summer (28.58 kg with 20 PV-30 batteries in June, 47.69 kg with 30 PV-20 batteries in July, and 66.96 kg with 40 PV-30 batteries in August). On the other hand, hydrogen produced is lower in winter (11.60 kg with 20 PV-10 batteries in December, 21.82 kg with 30 PV-20 batteries in January, and 32.20 kg with 40 PV-30 batteries in February). In addition, an economic analysis was conducted for the hybrid system. • Hybrid system based on solar and hydrogen energy is investigated. • A new energy management strategy is determined for the hybrid system. • Minimum of 5 PV-7 batteries are needed to meet the electricity needs of the house. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
31. Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks.
- Author
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Lee, Donghun, Kim, Jongeun, Kim, Suhee, and Kim, Kwanho
- Subjects
- *
RECURRENT neural networks , *ELECTRIC power consumption , *MULTIPURPOSE buildings , *ENERGY consumption , *ENERGY consumption of buildings , *COLLEGE buildings , *ENERGY management - Abstract
As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model's performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
32. Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models.
- Author
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Yao, Ching-Bang and Kao, Chang-Yi
- Subjects
ENERGY demand management ,SMART cities ,ENERGY industries ,ENERGY conservation ,RETAIL industry ,ENERGY consumption ,ELECTRIC power consumption - Abstract
The energy discourse is multifaceted, encompassing energy creation, storage, and conservation. Beyond the imperative of conserving energy consumption, effective energy management is a critical aspect of achieving overall energy efficiency. Despite being traditionally regarded as low electricity consumers, retailers play a pivotal role in economic activity. While categorized as non-productive energy users, the retail industry operates numerous establishments, facing substantial energy costs that make energy management integral to its operations. Historically, smaller retail stores have lacked awareness of energy saving. However, by connecting these stores, even modest reductions in individual electricity consumption can yield significant overall energy savings. This study aims to investigate the feasibility of implementing the demand-side management (DSM) aggregator model in the retail industry. Through surveys on awareness of energy saving and the application of deep learning techniques to analyze the effectiveness of the Aggregator model, the results reveal that the mean squared prediction error (MSPE) of this research is below 2.05%. This indicates substantial accuracy and offers meaningful reference value for Energy Service Company (ESCO) providers. The findings contribute practical recommendations for the sustainable and competitive implementation of DSM energy management practices in smart cities. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization.
- Author
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Al-Jamimi, Hamdi A., BinMakhashen, Galal M., Worku, Muhammed Y., and Hassan, Mohamed A.
- Subjects
DEEP learning ,FORECASTING ,ENERGY industries ,ENERGY consumption ,MACHINE learning ,PREDICTION models ,ENERGY management ,ELECTRIC power consumption - Abstract
Accurate load forecasting is of utmost importance for modern power generation facilities to effectively meet the ever-changing electricity demand. Predicting electricity consumption is a complex task due to the numerous factors that influence energy usage. Consequently, electricity utilities and government agencies are constantly in search of advanced machine learning solutions to improve load forecasting. Recently, deep learning (DL) has gained prominence as a significant area of interest in prediction efforts. This paper introduces an innovative approach to electric load forecasting, leveraging advanced DL techniques and making significant contributions to the field of energy management. The hybrid predictive model has been specifically designed to enhance the accuracy of multivariate time series forecasting for electricity consumption within the energy sector. In our comparative analysis, we evaluated the performance of our proposed model against ML-based and state-of-the-art DL models, using a dataset obtained from the Distribution Network Station located in Tetouan City, Morocco. Notably, the proposed model surpassed its counterparts, demonstrating the lowest error in terms of the Root-Mean-Square Error (RMSE). This outcome underscores its superior predictive capability and underscores its potential to advance the accuracy of electricity consumption forecasting. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. AN ANALYSIS OF ENERGY DEMAND IN IOT INTEGRATED SMART GRID BASED ON TIME AND SECTOR USING MACHINE LEARNING.
- Author
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MANAGRE, Jitendra and GUPTA, Namit
- Subjects
DEMAND forecasting ,MACHINE learning ,ENERGY consumption ,TIME management ,ELECTRIC power consumption ,SMART meters ,COMMUNICATION infrastructure - Abstract
Smart Grids (SG) encompass the utilization of large-scale data, advanced communication infrastructure, and enhanced efficiency in the management of electricity demand, distribution, and productivity through the application of machine learning techniques. The utilization of machine learning facilitates the creation and implementation of proactive and automated decision-making methods for smart grids. In this paper, we provide an experimental study to understand the power demands of consumers (domestic and commercial) in SGs. The power demand source is considered a smart plug reading dataset. This dataset is large dataset and consists of more than 850 user plug readings. From the dataset, we have extracted two different user data. Additionally, their hourly, daily, weekly, and monthly power demand is analysed individually. Next, these power demand patterns are utilized as a time series problem and the data is transformed into 5 neighbour problems to predict the next hour, day, week, and month power demand. To learn from the transformed data, Artificial Neural Network (ANN) and Linear Regression (LR) ML algorithms are used. According to the conducted experiments, we found that ANN provides more accurate prediction than LR Additionally, we observe that the prediction of hourly demand is more accurate than the prediction of daily, weekly, and monthly demand. Additionally, the prediction of each kind of pattern needs an individually refined model for performing with better accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. An overview of AC and DC microgrid energy management systems.
- Author
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Almihat, Mohamed G. Moh
- Subjects
- *
ENERGY management , *REACTIVE power , *POWER resources , *ELECTRIC power consumption , *MICROGRIDS , *ELECTRIC power distribution grids , *DISTRIBUTED power generation , *MAXIMUM power point trackers , *AC DC transformers - Abstract
In 2022, the global electricity consumption was 4,027 billion kWh, steadily increasing over the previous fifty years. Microgrids are required to integrate distributed energy sources (DES) into the utility power grid. They support renewable and nonrenewable distributed generation technologies and provide alternating current (AC) and direct current (DC) power through separate power connections. This paper presents a unified energy management system (EMS) paradigm with protection and control mechanisms, reactive power compensation, and frequency regulation for AC/DC microgrids. Microgrids link local loads to geographically dispersed power sources, allowing them to operate with or without the utility grid. Between 2021 and 2028, the expansion of the world's leading manufacturers will be driven by their commitment to technological advancements, infrastructure improvements, and a stable and secure global power supply. This article discusses iterative, linear, mixed integer linear, stochastic, and predictive microgrid EMS programming techniques. Iterative algorithms minimize the footprints of standalone systems, whereas linear programming optimizes energy management in freestanding hybrid systems with photovoltaic (PV). Mixed-integers linear programming (MILP) is useful for energy management modeling. Management of microgrid energy employs stochastic and robust optimization. Control and predictive modeling (MPC) generates energy management plans for microgrids. Future microgrids may use several AC/DC voltage standards to reduce power conversion stages and improve efficiency. Research into EMS interaction may be intriguing. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. Selected voltage control methods in LV local distribution grids with high penetration of PV.
- Author
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SZCZĘŚNIAK, Paweł, POWROŹNIK, Piotr, and SZTAJMEC, Elżbieta
- Subjects
VOLTAGE control ,SCIENTIFIC literature ,RENEWABLE energy sources ,ELECTRIC power consumption ,ELECTRIC power distribution grids ,ENERGY consumption ,MICROGRIDS - Abstract
Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
37. Efficient Demand Side Management Using a Novel Decentralized Building Automation Algorithm.
- Author
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Spagkakas, Christodoulos, Stimoniaris, Dimitrios, and Tsiamitros, Dimitrios
- Subjects
- *
LOAD management (Electric power) , *INTELLIGENT buildings , *ENERGY management , *ELECTRIC power consumption , *CONSUMPTION (Economics) , *CLEAN energy - Abstract
Given its adaptable and efficient energy consuming devices during peak hours, the residential building sector is urged to take part in demand response (DR) initiatives with the use of a building energy management system (BMS). The residents of buildings with BMS enjoy secure, pleasant, and fully managed lifestyles. Although the BMS helps the building consume less energy and encourages occupant engagement in energy-saving initiatives, unwelcome interruptions and harsh instructions from the system are inconvenient for the inhabitants, which further discourages their participation in DR initiatives. Building automation control is a crucial factor for improving buildings' energy efficiency and management, as well as improving the electricity grid's reliability indices. Smart houses that use the right sizing procedure and energy-management techniques can help lower the demand on the entire grid and potentially sell clean energy to the utility. Recently, smart houses have been presented as an alternative to traditional power-system issues including thermal plant emissions and the risk of blackouts brought on by malfunctioning bulk plants or transmission lines. This paper describes the necessary technology requirements and presents the methodology and the decentralized building automation novel algorithm for efficient demand side management in a building management system. Human comfort aspects including thermal comfort and visual comfort were taken into consideration when selecting heating and lighting controls. The suggested BMS relies primarily on a load-shifting technique, which moves controllable loads to low-cost periods to avoid high loading during peak hours. The model aims to minimize the individual household electricity consumption cost while considering customers' comfort and lifestyle. All these are applied in an experimental university microgrid, and the results are presented in terms of energy saving in kWh, money in €, and working hours. The results demonstrated that the proposed approach might successfully lower energy use during the DR period and enhance occupant comfort. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Decision tree-based prediction approach for improving stable energy management in smart grids.
- Author
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Chen, Sichao, Huang, Liejiang, Pan, Yuanjun, Hu, Yuanchao, Shen, Dilong, and Dai, Jiangang
- Subjects
- *
DECISION trees , *EVOLUTIONARY computation , *ENERGY management , *METAHEURISTIC algorithms , *DATA transmission systems , *MAGAZINE design , *ELECTRIC power consumption , *SMART meters - Abstract
Today, the Internet of Things (IoT) has an important role for deploying power and energy management in the smart grids as emerging trend for managing power stability and consumption. In the IoT, smart grids has important role for managing power communication systems with safe data transformation using artificial intelligent approaches such as Machine Learning (ML), evolutionary computation and meta-heuristic algorithms. One of important issues to manage renewable energy consumption is intelligent aggregation of information based on smart metering and detecting the user behaviors for power and electricity consumption in the IoT. To achieve optimal performance for detecting this information, a context-aware prediction system is needed that can apply a resource management effectively for the renewable energy consumption for smart grids in the IoT. Also, prediction results from machine learning methods can be useful to manage optimal solutions for power generation activities, power transformation, smart metering at home and load balancing in smart grid networks. This paper aims to design a new periodical detecting, managing, allocating and analyzing useful information regarding potential renewable power and energy consumptions using a context-aware prediction approach and optimization-based machine learning method to overcome the problem. In the proposed architecture, a decision tree algorithm is provided to predict the grouped information based on important and high-ranked existing features. For evaluating the proposed architecture, some other well-known machine learning methods are compared to the evaluation results. Consequently, after analyzing various components by solving different smart grids datasets, the proposed architecture's capacity and supremacy are well determined among its traditional approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Intelligent energy management systems: a review.
- Author
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Mischos, Stavros, Dalagdi, Eleanna, and Vrakas, Dimitrios
- Subjects
ENERGY management ,INTELLIGENT buildings ,COMMERCIAL buildings ,ARTIFICIAL intelligence ,ELECTRIC power consumption ,CARBON emissions ,ECOLOGICAL impact ,ENERGY consumption - Abstract
Climate change has become a major problem for humanity in the last two decades. One of the reasons that caused it, is our daily energy waste. People consume electricity in order to use home/work appliances and devices and also reach certain levels of comfort while working or being at home. However, even though the environmental impact of this behavior is not immediately observed, it leads to increased CO2 emissions coming from energy generation from power plants. It has been shown that about 40% of these emissions come from the electricity consumption and also that about 20% of this percentage could have been saved if we started using energy more efficiently. Confronting such a problem efficiently will affect both the environment and our society. Monitoring energy consumption in real-time, changing energy wastage behavior of occupants and using automations with incorporated energy savings scenarios, are ways to decrease global energy footprint. In this review, we study intelligent systems for energy management in residential, commercial and educational buildings, classifying them in two major categories depending on whether they provide direct or indirect control. The article also discusses what the strengths and weaknesses are, which optimization techniques do they use and finally, provide insights about how these systems can be improved in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. Messtechnik im Einsatz.
- Subjects
PHOTOVOLTAIC power systems ,ENERGY management ,ELECTRIC power consumption ,PEAK load ,ENERGY consumption ,CERTIFICATION ,SAFETY standards ,PHOTOVOLTAIC power generation - Abstract
Copyright of DE: Das Elektrohandwerk is the property of Hüthig GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
41. Data consumption-based home energy management for residential platform.
- Author
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Hussain, Rusul H. and Shujaa, Mohamed Ibrahim
- Subjects
- *
ENERGY consumption , *ENERGY management , *PARTICLE swarm optimization , *PEAK load , *ELECTRONIC equipment , *ELECTRIC power consumption - Abstract
The proposed plan aims to aid and encourage efficient use of electricity and, as a result, minimize power consumption to the barest minimum. The key feature of the proposed system is the ability to remotely monitor and manage electrical and electronic appliances to reduce peak load to the barest minimum. The plan has both efficiency and accuracy advantages also financial advantages. Analytical Method (Am) is employed to optimize the load consumption of a typical residential building. Dwelling based on accurate data. Smart plugs capture the consumption data because they were created and supplied to work with innovative home products. Based on the modeling findings, the energy consumption expense bill reduces by 24.31 percent to minimize peak load by 32.02 percent. Analytical process, Bacterial Foraging Optimization (BFO), Particle Swarm Optimization (PSO), and other optimization approaches are all put up against the proposed way (AM). [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
42. Energy cost and carbon emission minimization for hybrid grid-independent microgrid using rule-based energy management scheme.
- Author
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Bukar, Abba Lawan, Tan, Chee Wei, Lau, Kwan Yiew, Toh, Cheun Ling, Ayop, Razman, Abbagoni, Baba Musa, and Dahiru, Ahmed Tijjani
- Subjects
- *
ENERGY industries , *MICROGRIDS , *CARBON emissions , *ENERGY management , *ELECTRIC power consumption - Abstract
This article presents a rule-based energy management (EM) scheme for a hybrid grid-independent microgrid. The microgrid incorporate a wind turbine (WT), photovoltaic (PV) panels, battery (BT) bank and diesel generator (Dgen). The rule-based algorithms had been widely applied in electric vehicle research because they are preferable for real-time EM and are computationally efficient. Based on this motivation, the application of the rule-based algorithm is applied to the design microgrid in this paper. The features EM scheme includes uninterrupted electricity supply to demand, minimize fuel consumption and minimize BT bank degradation. To simulate the microgrid, models for the state-of-charge (SOC) estimation of the BT bank, WT, PV and Dgen is developed. The uninterrupted supply is realized by managing the energy flow of the microgrid energy sources and setting the minimum SOC of the BT bank at 30%. The resiliency of the scheme is validated under fluctuating demand and weather conditions, considering cold, hot and rainy seasons. Simulation results have demonstrated the capability of the rule-based EM scheme in achieving high feasibility and effectiveness despite the intermittent sources and fluctuating demand. Additionally, the proposed EM scheme has significantly minimized CO2 emission from 56.3 tons/year to 4.3 tons/year and the cost of energy from $1.8/kWh to $0.43/kWh. This led to a reduction of the CO2 emission by 92.4% and the COE is minimize by 76.11%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
43. Energy management system using dual-layer approach for residential micro-grid system under fixed load condition.
- Author
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Aqilah, Syahirah, Rosmin, Norzanah, Mustaamal, Aede Hatib, Hasan, Norshahida, and Aripriharta
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- *
ENERGY management , *MICROGRIDS , *SUPPLY & demand , *ELECTRIC power consumption , *STRATEGIC planning - Abstract
This paper proposes a dual-layer coordination of energy management system (EMS) in stand-alone mode micro-grid (MG) system that considering demand response (DR) scheme in the means to reduce electricity cost at power and demand side. The main advantage of this method is that the coordination between power side and demand side can be balanced. While ensuring that operations at power side is economically controlled, the satisfaction of load demands can also be achieved. In this work, two strategies have been implemented, load shifting process based on pricing strategy as well as micro sources scheduling strategy. The performance of proposed approach has been tested based on the fixed load conditions. For the first layer EMS, the goal is set to reduce the electricity bill through load shifting process. In this EMS, five different selective modes of operation were considered during load shifting strategy implementation. In the second layer, the load profile from 20 houses has been used as the input data and aim has been paid to ensure the MG operates economically while ensuring a continuous electricity supply to the demand sides. For the economical consideration, the operation and maintenance (O&M) cost of MG also included. From the executed simulation, it has been found that from the first layer strategy, the maximum cost saving of electricity bill that able to be achieved is around 2.71%. In the second layer EMS however, the cost of O&M saving for fixed load condition is around 1.92%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Modeling of energy management with electric load curve using fuzzy logic system for office electricity consumers.
- Author
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Septianto, Haris Dwi, Abdullah, Ade Gafar, Hakim, Dadang Lukman, and Zakaria, Diky
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- *
FUZZY logic , *ENERGY management , *FUZZY systems , *ELECTRIC power consumption , *CONSUMERS - Abstract
Currently, the need for electrical energy continues to increase. Therefore, a detailed pattern of electricity use is needed to change a consumer's use of electricity and reduce global energy consumption. In energy management, electricity loading is needed to regulate if there is an increase in load in the electricity sector. So that the electrical load curve can detect a peak load from excessive energy use. This research is a fuzzy logic artificial intelligence method to process a rule of input variables and produce an energy consumption that has been used. In the resulting energy consumption, the electric load curve is influenced by variable factors such as the period of use of the equipment and the number of users. The input data used for this fuzzy logic system is the actual data of the period of use of the equipment used every day and the number of users using the equipment. From the results of the study, it was found that the level of accuracy between the actual data and fuzzy logic data will be obtained from the mean absolute percentage error (MAPE). It is hoped that this research can be a modeling of an electrical energy detection system in a residence or office because it will facilitate the use of energy in an equipment. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
45. Utility Consumption Monitoring for Sustainability: To optimize use of industrial electricity, water, liquid fuel and gases, operations teams need reliable measurement paired with energy management systems.
- Author
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Marcon, Cory
- Subjects
ENERGY management ,ENERGY consumption ,LIQUID fuels ,ELECTRIC power consumption ,GAS as fuel ,LIQUEFIED gases - Published
- 2023
46. Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks.
- Author
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Parangusam, Kanakaraj, Lekshmana, Ramesh, Gono, Tomas, and Gono, Radomir
- Subjects
- *
INTELLIGENT control systems , *PEAK load , *ENERGY management , *ELECTRIC power consumption , *SUMMER , *RESIDENTIAL mobility - Abstract
Electricity demand has increased tremendously in recent years, due to the fact that all sectors require energy for their operation. Due to the increased amount of modern home appliances on the market, residential areas consume a significant amount of energy. This article focuses on the residential community to reduce peak load on residential distribution networks. Mostly, the residential consumer's power demand increases more during the summer season due to many air conditioners (AC) operating in residential homes. This paper proposes a novel summer peak intelligent controller (SPIC) algorithm to reduce summer peak load in residential distribution transformers (RDT). This proposed SPIC algorithm is implemented in a multi-home energy management system (MHEMS) with a four-home hardware prototype and a real-time TNEB system. This hardware prototype is divided into two different cases, one with and one without taking user comfort into account. When considering consumer comfort, all residential homes reduce their peak load almost equally. The maximum and minimum contribution percentages in Case 2 are 29.82% and 19.30%, respectively. Additionally, the real-time TNEB system is addressed in two different cases: with and without incentive-based programs. In the real-time TNEB system during peak hours, the novel SPIC algorithm reduces peak demand in Case 1 by 113.70 kW, and Case 2 further reduces it to 118.80 kW. The peak load decrease in Case 2 during peak hours is 4.5% greater than in Case 1. In addition, we conducted a residential consumer opinion survey to validate the acceptance rate of the proposed design and algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
47. Activating electricity system demand response for commercial and industrial organisations.
- Author
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Lashmar, N., Wade, B., Molyneaux, L., and Ashworth, P.
- Subjects
ELECTRIC power consumption ,ENERGY consumption ,INDUSTRIAL energy consumption ,ENERGY management ,RENEWABLE energy sources ,CONSUMPTION (Economics) ,PRICE increases ,ENVIRONMENTAL impact analysis - Abstract
With the rapid uptake of renewable energy generation and increasing price volatility, there are multiple opportunities emerging for businesses to earn additional revenue and reduce electricity bills by implementing demand response. However, commercial and industrial consumer implementation of demand response is not well understood and largely absent in energy management guidelines, which focus on reducing energy consumption and driving energy efficiency. Based on interviews with managers from 24 commercial and industrial businesses, we describe a practical implementation framework for demand response. The framework identifies unique implementation features for demand response - the activation steps. Energy management guidelines may have underemphasised approaches to demand response and inclusion of its unique features for businesses because benefits to be gained from demand response have focussed on benefits for the utilities in the electricity system, not benefits for individual businesses. The article concludes there is an opportunity for market operators to encourage organisations who produce energy management guidelines to include demand response, to promote awareness of the opportunities for businesses and provide practical guidance for implementation, therefore providing support for greater renewable energy penetration and reduced energy costs for businesses. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System.
- Author
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Ma, Ping, Cui, Shuhui, Chen, Mingshuai, Zhou, Shengzhe, and Wang, Kai
- Subjects
- *
ENERGY management , *DEEP learning , *FORECASTING , *ELECTRIC power consumption , *ENERGY consumption , *FEATURE extraction - Abstract
With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
49. Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review.
- Author
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de Oliveira Cavalcanti, Gleydson and Pimenta, Handson Claudio Dias
- Subjects
- *
ENERGY management , *INTERNET of things , *ELECTRIC power consumption , *BIBLIOMETRICS , *ENERGY consumption , *THEMATIC analysis - Abstract
The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
50. Towards efficient human–machine interaction for home energy management with seasonal scheduling using deep fuzzy neural optimizer.
- Author
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Javaid, Sakeena, Javaid, Nadeem, Alhussein, Musaed, Aurangzeb, Khursheed, Iqbal, Sohail, and Anwar, Muhammad Shahid
- Subjects
- *
CONSUMER behavior , *ENERGY management , *PRODUCTION scheduling , *CONSUMPTION (Economics) , *FUZZY logic , *ELECTRIC power consumption , *SMART power grids , *SUMMER - Abstract
Maintaining the records of domestic consumers' electricity consumption patterns is very complex task for the utilities, especially for extracting the meaningful information to maintain their demand and supply. Due to the increase in population, large amount of valuable data from the domestic sector is extracted by the smart meters and it becomes a vulnerable issue to tackle this information in recent era. In this work, we have proposed the fuzzy deep neural optimizer to optimize the cost and power demand of the stochastic behavior of the domestic consumers. For optimization process, this optimizer considers three control parameters: energy consumption, time of the day, and price and two performance parameters: cost and peak reduction. The dataset used for this optimization process is of two seasons: summer and winter season and it is obtained from Pecan Street Incorporation site. Takagi Sugeno fuzzy inference system is applied for the computation of the rules, which are formulated using the Membership Functions (MFs) of the aforementioned parameters. The nature of the MFs is chosen as Gaussian MFs to continuously monitoring the consumers' behaviors at different time intervals. Simulations are performed to show the robustness of the proposed optimizer in terms of energy efficiency and cost optimization up to 8 kWh and 1$ for the summer season and 12.5 kWh and 4$ for winter season. The proposed optimizer outperforms the previous scheme with remarkable results and highly recommended for the future systems where consumers are growing tremendously. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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