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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

As renewable energy sources and microgrids have developed, the need to use a transversal approach for dealing with issues on the design and energy management of these assets is clear. Although renewable energy sources and microgrids are used within the electricity sector in a significant number of countries, the technical and scientific literature often dismisses the effects that in-force regulatory frameworks have on this type of assets. This Special Issue aims to enlighten readers on the relationship between the electricity sector's regulatory frameworks and microgrids, providing clear evidence on the need to consider regulatory constraints in their design and energy management. [ABSTRACT FROM AUTHOR]

European manufacturing companies are currently in a very difficult situation. On the one hand, they have to strive for sustainable development. On the other hand, they have to face problems related to the geopolitical situation and the pursuit of cleaner production (including restrictive regulations on emissions). A significant increase in electricity prices means that companies have to take steps to use electricity rationally without reducing the level of production or incurring other additional costs. In this study, attention was paid to the influence of quality tools (QTs) in production process (PP) management on increasing energy efficiency (EE). Based on the literature review, it was found that the most frequently described QTs, i.e., SMED, TPM, Six Sigma, SPC, FMEA, 5S, or Poka-Yoke, used to improve PP did not take into account their impact on the EE of the enterprises. Therefore, the main purpose of this study was to examine this correlation and show that QTs can be helpful in shaping the energy management system (EMS). Achieving the research goal was possible thanks to surveys carried out among manufacturing companies. Using statistical tests, the existence of a statistically significant correlation was proved between QTs and EMS elements. [ABSTRACT FROM AUTHOR]

Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives. [ABSTRACT FROM AUTHOR]

In microgrids (MGs), energy management systems (EMSs) have been using increasingly detailed models of generation units, loads, and networks to make decisions on the power/energy contribution of each available unit to meet the electrical energy demand. This work aims to investigate the use of digital twins (DT) of small productive processes (SPPs) to regulate endogenous process variables to ensure final product quality, while the expected power consumption is estimated and communicated to the EMS so that it can make its decisions on the participation of each power source in meeting the electrical energy demand. The literature review reveals that this is one of the first attempts, in the context of MGs, to generate DT for SPPs that combine not only the electrical energy consumption, but also link it with the energy/mass balances taking place in the SPPs, highlighting the complexity that SPPs have as electrical loads. The results demonstrate that environmental conditions significantly influence the final electrical consumption of the SPPs. Additionally, the MG exhibits better economic performance when the SPP DT supports EMS decision-making, which is of great importance in MGs due to the special conditions they have for electric power generation, being more challenging in isolated MGs. [ABSTRACT FROM AUTHOR]

Microgrids have become an alternative for integrating distributed generation to supply energy to isolated communities, so their control and optimal management are important. This research designs and simulates the three levels of control of a DC microgrid operating in isolated mode and proposes an Energy Management System (EMS) based on Model Predictive Control (MPC), with real-time measurement feedback for optimal energy dispatch, which ensures power flow distribution and operation at minimum cost while extending the lifespan of the BESS. The EMS can react to disturbances produced in the lower control levels. The microgrid's performance is analyzed and compared in two scenarios without EMS, and with EMS against changes in irradiation and changes in electricity demand. The fulfillment of the power balance is evaluated by analyzing the power delivered by each generation unit, the operating cost, and the state of charge of the battery (SOC). [ABSTRACT FROM AUTHOR]

A smart grid (SG), considered as a future electricity grid, utilizes bidirectional electricity and information flow to establish automated and widely distributed power generation. The SG provides a delivery network that has distributed energy sources, real-time asset monitoring, increased power quality, increased stability and reliability, and two-way information sharing. Furthermore, SG provides many advantages, such as demand response, distribution automation, optimized use of electricity, economical energy, real-time grid status monitoring, voltage regulation or VAR control, and electricity storage. In this survey, we explore the literature on smart Grid enabling technologies until 2022. We dig out four major systems: (1) the smart grid's prominent features and challenges; (2) the smart grid standard system and legislations; (3) smart grid energy subsystem; and (4) the smart grid management system and protection system for new researchers for their future projects. The research challenges and future recommendations are also presented in the conclusion section to explore the new paradigm. [ABSTRACT FROM AUTHOR]

As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles. [ABSTRACT FROM AUTHOR]

To reduce the on-peak electrical power consumption, storage devices are widely performed with the help of an energy management system. According to IEA, residential air conditioning consumes 70% of the electricity, increasing by 4% every year. To minimize peak power consumption, thermal energy storage (TES) can be used to store cooled water for the air conditioning system. An efficient chilled water tank was designed and computationally investigated. Three-dimensional cylindrical tanks were simulated with seven different heights to diameter (H:D) ratios. At first, the temperature changes in a chilled water tank during discharging and charging periods were studied. An 11-h charging period was carried out during the off-peak time at night, while the discharging period was 13 h during the daytime. Under time constraints regarding peak and off-peak periods, a tank with an H:D = 2.0 can only be used for 13-h discharging. Then the chilled water was simulated with a set temperature of 4 °C during the charging. This resulted in the system being usable for six days, after which it had to be stopped for longer charging. A storage tank with an H:D ratio of 2.0 was found to be suitable for an air conditioning system. If six days of operations (one day off) were used, it could save 15.38% of electrical energy consumption and 51.65% of electricity cost. This saving leads to a 5.55-year payback period. [ABSTRACT FROM AUTHOR]

The rising demand for electricity, economic benefits, and environmental pressures related to the use of fossil fuels are driving electricity generation mostly from renewable energy sources. One of the main challenges in renewable energy generation is uncertainty involved in forecasting because of the intermittent nature of renewable sources. The demand also varies according to the time of day, the season, the location, the climate, and the availability of resources. Microgrids offer a potential solution for the integration of small-scale renewable energy sources and loads along with energy storage systems and other non-renewable sources. However, intermittent generation and varying demand need to be matched to provide stable power to consumers. Therefore, it is crucial to design an energy management system to effectively manage the energy sources and supply loads for reliable and efficient operation. This paper reviews different techniques proposed in the literature to achieve the objectives of a microgrid energy management system. The benefits of existing energy management systems and their challenges are also discussed. The challenges associated with uncertainties and methods to overcome them are critically reviewed. [ABSTRACT FROM AUTHOR]

In response to the future net zero emissions plan in Taiwan, the campus shoulders the university's social responsibility and educational significance. Recently, energy conservation and carbon reduction have become common goals for every campus. However, there is a common problem to be addressed in terms of how to take into account the economic benefits and the continuous improvement strategy regarding actual demands. In this study, a systematic design of energy-saving action plans for a Taiwan campus by considering economic benefits and actual demands is demonstrated. By taking National Taiwan University of Science and Technology in Taiwan as an example, eight energy-saving action plans during the period from 2016 to 2020 are introduced, and the effectiveness of these topologies is verified by real implementations. Action plans contain the installation of a smart energy management system, elevator power recovery devices, circulating fans, and lighting delay switches; the replacement of old air-conditioners, fluorescent lamps, and high-sodium streetlights; and load consolidation and low-efficiency transformers replacement. Upon evaluation, the estimated electricity savings can achieve up to 16% of annual electricity consumption, and the payback period is about 5.22 years. Upon application, the actual power saving amounts to about 3,076,260 kWh for approximately 15.5% of the power consumption of Taiwan Tech in 2020. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy-based energy management strategy (EMS) to maximize the self-consumption from a PV installation with an energy storage system (ESS) for the residential sector adapted to the Ecuadorian electricity market. The EMS includes two control levels: Energy management at the end-user level (Fuzzy-based EMS and optimized by genetic Algorithm) and Energy management at the distribution grid level (Fuzzy-based EMS). Both strategies aim to maximize the use of the energy generated at home (taking into account the local solar generation profile), fulfilling the loads' demand and injecting the energy surplus into the main grid to be economically compensated. Additionally, this paper presents economical modeling according to the electricity market in Ecuador. The main results showed a cost reduction in the electricity bill up to 83.64% from the base case (residential consumption without a PV system). In the scenario of a community electricity market (still not contemplated under the Ecuadorian electricity law), the potential economic savings may be more than double compared to the exact case but only with a self-consumption system. [ABSTRACT FROM AUTHOR]

It is critical to address energy issues as we move through the first half of the twenty-first century, as societies become firmly aware of the consequences of resource scarcity and the disastrous consequences of climate change from human activity (especially heavy industry). We need new systems at the power supply and energy conversion levels, new approaches and solutions for transforming energy usage across manufacturing plants and new energy-efficient, greener technologies in companies across industries (particularly from those intensively consuming energy). Incremental changes in industry, across energy-consuming sectors and on all the levels - from industry policies to operational processes in companies - seem to be indispensable. Globally, some industries are large electricity purchasers, yet the consumption of energy greatly differs between countries, (i.e., the energy unit consumption in the pulp and paper industry noticeably varies among the 27 countries of the EU by a factor of 2-3 [[3]], with one of the primary reasons being production methods). [Extracted from the article]

This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces. [ABSTRACT FROM AUTHOR]

Since no solutions have been proposed in Colombia that seek to reduce the consumption of electricity at the residential level, this paper describes the design and implementation of a simple prototype of a low-cost home energy management system (HEMS). The objective of this platform is to monitor the energy consumption of typical household devices so that users can access the consumption of each device separately and then establish the strategy that allows them to reduce energy consumption at home. In order to demonstrate that our system is viable, the system has been evaluated by measuring weekly energy consumption with the on-line and off-line HEMS using a test bench with typical household devices in a Sincelejo typical household. The evaluation has shown that with the installation of this HEMS, consumption is reduced by 27%. This shows that it is possible to achieve a good reduction percentage with a low-cost system. [ABSTRACT FROM AUTHOR]

The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions. [ABSTRACT FROM AUTHOR]

This article aims to present changes in the use of electricity by service companies, resulting from regulations within the framework of increasing energy efficiency from the perspective of the implementation of the European Green Deal strategy. To achieve the above goal, the following research question was formulated: to what extent did the COVID-19 pandemic affect the implementation of energy transformation and electricity consumption among the surveyed group of recipients? It should be noted that, so far in the global environment, more and more electricity has been used every year, and this tendency is still continuous and growing. Therefore, in European Union countries, measures have been taken to balance demand and its rational use, resulting from the implementation of the European Green Deal strategy. According to the strategic goal of the indicated policy, EU countries are obliged to implement a sequence of actions enabling their transformation into a modern, resource-efficient, and competitive economy. In particular, the strategy aims to achieve three main goals: 1. Achieving climate neutrality by reducing net greenhouse gas emissions to zero in 2050; 2. Decoupling economic growth from the consumption of natural resources; and 3. Striving for an equal standard of living in all countries. Therefore, the behavior of individual countries should focus on providing the required amount of energy to ensure socioeconomic growth while reducing pollution and environmental devastation caused by traditional methods of energy production and use. There are numerous talks and debates about the defined tasks and mechanisms leading to the achievement of the indicated goals, in which hardly any mention is made of the methods of monitoring the progress and evaluation of individual projects at the stage of building a new green deal. This study aims to fill the research gap observed in the current state of knowledge on energy management in EU countries from the perspective of the European Green Deal strategy and changes in its management resulting from the conditions of the COVID-19 pandemic. Therefore, in line with the aim of the article: 1. Factors influencing electricity consumption in service enterprises operating in EU countries were indicated; 2. Energy consumption variability in these entities was determined; and 3. The correlation between electricity consumption and GDP growth in the service sector located in EU countries was indicated. Therefore, a hybrid research method was used to analyze the data obtained from the databases of Eurostat and Statistics Poland, which consisted of the following analyses: diagnostic-descriptive, main components, and wavelet transform. Based on the conducted research, it should be concluded that energy consumption among service companies operating in the EU market is conditioned by three sources of causes along with the relevant consumption factors. The first group includes energy and technical premises, along with technological determinants. The second is shaped by financial and economic motives, which include socioeconomic factors. The third group is related to environmental sources represented by the natural environment and geographic and meteorological factors. The increase in electricity consumption in service enterprises is related to the average GDP growth of EU countries following a two-way cause-and-effect relationship implemented until 2019. Nevertheless, since 2020, there has been a noticeable decrease in energy consumption by 14.01% by service entities, which results from the limitations caused by the COVID-19 pandemic and the implementation of the European Green Deal strategy. Meanwhile, the structure of electricity consumption growth was dominated by industrial enterprises (increased by 37.7%) and individual consumers (increased by 7.8%). The results of the research may motivate the managers of EU countries and enterprises to analyze the factors of energy consumption, its variability, and dependence on economic growth, which contribute to determining the forecast of future energy demand, in connection with the ongoing energy transformation resulting from the implementation of the European Green Deal strategy, and economic slowdown caused by the COVID-19 pandemic. The issues presented in this article are an attempt to fill the gap indicating practical experience related to the process of electricity management and management in the political, economic, and technological dimensions from the perspective of implementing the European Green Deal strategy and the conditions resulting from the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Smart meters for measuring electricity consumption are fast becoming prevalent in households. The meters measure consumption on a very fine scale, usually on a 15 min basis, and the data give unprecedented granularity of consumption patterns at household level. A multitude of papers have emerged utilizing smart meter data for deepening our knowledge of consumption patterns. This paper applies a modification of Okoli’s method for conducting structured literature reviews to generate an overview of research in electricity customer classification using smart meter data. The process assessed 2099 papers before identifying 34 significant papers, and highlights three key points: prominent methods, datasets and application. Three important findings are outlined. First, only a few papers contemplate future applications of the classification, rendering papers relevant only in a classification setting. Second; the encountered classification methods do not consider correlation or time series analysis when classifying. The identified papers fail to thoroughly analyze the statistical properties of the data, investigations that could potentially improve classification performance. Third, the description of the data utilized is of varying quality, with only 50% acknowledging missing values impact on the final sample size. A data description score for assessing the quality in data description has been developed and applied to all papers reviewed. [ABSTRACT FROM AUTHOR]

Smart grid technology has given users the ability to regulate their home energy use more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduce a metaheuristic-based HEM system which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on the EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB were used to test the performance of the proposed hybrid technique. The simulations were run for multiple homes with multiple appliances, which were categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the time-varying retail pricing enabled by the smart grid two-way communication infrastructure algorithms EWA and HSA, along with a Real-Time Price scheme. These techniques helped us to find the best usage pattern for energy consumption to reduce electricity costs. These metaheuristic techniques efficiently reduced and shifted the load from peak hours to off-peak hours and reduced electricity costs. In comparison to HSA, the simulation results suggest that EWA performed better in terms of cost reduction. In comparison to EWA and HSA, HSA was more efficient in terms of PAR. However, the proposed hybrid approach EHSA gave the maximum reduction in cost which was 2.668%, 2.247%, and 2.535% in the case of 10, 30, and 50 homes, respectively, as compared to EWA and HSA. [ABSTRACT FROM AUTHOR]

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization. [ABSTRACT FROM AUTHOR]

This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play a significant role in implementing smart energy. One of the approaches to implementing smart energy is to analyze consumption data and provide targeted contracts to consumers based on their individual consumption characteristics. To that end, the identification of individual consumption features is important for suppliers and utilities. Given the complexity of smart home load profiles, an appliance-based identification is nearly impossible. In this paper, we propose a different approach by grouping appliances based on their rooms; thus, we provide a room-based identification of energy consumption. To this end, this paper presents and tests an intelligent consumption identification methodology, that can be implemented in the form of an ensemble of artificial intelligence tools. The ensemble, which comprises four convolutional neural networks (CNNs) and four k-nearest neighbor (KNN) algorithms, is fed with smart submeter data and outputs the identified type of room in a given dwelling. Results obtained from real-world data exhibit the superiority of the ensemble, with respect to accuracy, as compared with individual CNN and KNN models. [ABSTRACT FROM AUTHOR]

A residential cogeneration system (CGS) is highlighted because of its efficient energy usage on both the supplier and consumer sides. It generates electricity and heat simultaneously; however, there is insufficient information on the efficiency according to the condition of usage. In this study, we analysed the performance data measured by the home energy management system (HEMS) and the lifestyle data of residents in a condominium of 356 flats where fuel cell CGS was installed in each flat. The electricity generated by CGS contributed to an approximately 12% reduction in primary energy consumption and CO2 emission, and the rate of generation by the CGS in the electric power demand (i.e., contribution rate) was approximately 38%. The electricity generation was mainly affected by the use of electricity up to 4 MWh/household/year. Gas or water use also impacted electric power generation, with water use as the primary factor affecting the contribution rate. Electric power generation changes monthly, mainly based on the water temperature. From these results, we confirmed that a CGS has substantial potential to reduce energy consumption and CO2 emission in condominiums. Thus, it is recommended for installation of fuel cell CGS in existing and new buildings to contribute to the energy-saving target of the Japanese Government in the residential sector. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have a lot of potential to play an essential role in the smart power grid. EVs not only can reduce the amount of emission yielded from fossil fuels but also can be considered as an energy storage system (ES) and a backup system. EVs could support the demand response (DR) strategy that is considered as utmost importance to shift electricity demand in peak hours. This article aims to assess the impact of the presence of EV on DR strategy in a home-microgrid (H-MG). In order to reach the optimal set point, our energy management system (EMS) has been merged with differential evolution (DE) method. The results were auspicious and showed that the proposed method could decrease market clearing price (MCP) by 26% and increase the performance of DR by 17%. [ABSTRACT FROM AUTHOR]

In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting. [ABSTRACT FROM AUTHOR]

With the increasing penetration of intermittent renewable energy generation, there is a growing demand to use the inherent flexibility within buildings to absorb renewable related disruptions. Heat pumps play a particularly important role, as they account for a high share of electricity consumption in residential units. The most common way of quantifying the flexibility is by considering the response of the building or the household appliances to external penalty signals. However, this approach neither accounts for the use cases of flexibility trading nor considers its impact on the prosumer comfort, when the heat pump should cover the stochastic domestic hot water (DHW) consumption. Therefore, in this paper, a new approach to quantifying the flexibility potential of residential heat pumps is proposed. This methodology enables the prosumers themselves to generate and submit the operating plan of the heat pump to the system operator and trade the alternative operating plans of the heat pump on the flexibility market. In addition, the impact of the flexibility provision on the prosumer comfort is investigated by calculating the warm water temperature drops in the thermal energy storage given heat demand forecast errors. The results show that the approach with constant capacity reservation in the thermal energy storage provides the best solution, with an average of 2.5 min unsatisfactory time per day and a maximum temperature drop of 2.3 °C. [ABSTRACT FROM AUTHOR]

The fast depletion of fossil fuels and the growing awareness of the need for environmental protection have led us to the energy crisis. Positive development has been achieved since the last decade by the collective effort of scientists. In this regard, renewable energy sources (RES) are being deployed in the power system to meet the energy demand. The microgrid concept (AC, DC) is introduced, in which distributed energy resources (DERs), the energy storage system (ESS) and loads are interconnected. DC microgrids are appreciated due to their high efficiency and reliability performance. Despite its significant growth, the DC microgrid is still relatively novel in terms of grid architecture and control systems. In this context, an energy management system (EMS) is essential for the optimal use of DERs in secure, reliable, and intelligent ways. Therefore, this paper strives to shed light on DC microgrid architecture, control structure, and EMS. With an extensive literature survey on EMSs' role, different methods and strategies related to microgrid energy management are covered in this article. More attention is centered on the EMS for DC microgrids in terms of size and cost optimization. A very concise analysis of multiple optimization methods and techniques has been presented exclusively for residential applications. [ABSTRACT FROM AUTHOR]

This paper presents the improvements of the hosting capacity of photovoltaics (PVs) and electric vehicles (EVs) in a stand-alone microgrid (MG) with an energy storage system (ESS) by consider-ing a model predictive control (MPC) based energy management system. The system is configured as an MG, including PVs, an ESS, a diesel generator (DG), and several loads with EVs. The DG is controlled to operate at rated power and the MPC algorithm is used in a stand-alone MG, which supplies the energy demanded for several loads with EVs. The hosting capacity of the load in-cluding the EV and PVs can be expanded through the ESS to the terminal node of the microgrid. In this case, the PVs and the load can be connected in excess of the capacity of the diesel genera-tor, and each bus in the feeder complies with the voltage range required by the grid. The effec-tiveness of the proposed algorithm to resolve the hosting capacity is demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events. [ABSTRACT FROM AUTHOR]

Although rail is one of the most sustainable transport systems, there is still room to reduce its energy demand. In particular, during the braking of DC powered trains, a significant amount of energy is wasted. The recent developments in energy storage system technologies, combined with the widely used technique of regenerative braking, can considerably increase energy saving. This paper explores this theme, quantifying the amount of braking energy that can be potentially recovered in a real case study, starting from the experimental data measured on-board train. A simplified numerical model of the recovery process has been implemented. Adopting it, the energy that can be saved, with one or two energy storage systems, has been quantified for each possible position along the track. The procedure allows to determine the optimal position. Further findings about the impact of voltage level on the efficiency of the recovery process have been reported. The optimal level of voltage has been determined, also considering the additional losses in the catenary, both during the traction and braking phase of the train. Moreover, it allows dimensioning of stationary storage systems considering two different energy management strategies and their impact on the peak of stored energy. The proposed approach will be presented with reference to the concrete case of a specific route on the Italian rail network, analyzing a train in normal commuter service and the obtained results will be discussed. In the best situation, about the 73% of the braking energy can be recovered. [ABSTRACT FROM AUTHOR]

To maximize the performance of power batteries and supercapacitors in a hybrid energy storage system (HESS) and to resolve the conflict between the high power demands of electric vehicles and the limitations of high-current charging and discharging of the power battery, a vehicle power demand model incorporating road slope information has been constructed. This paper takes a HESS composed of power battery and supercapacitor as the object, and a rule-based energy management strategy (EMS) based on road slope information is proposed to realize the reasonable distribution and management of energy under the slope condition. According to the slope information of the road ahead, the energy consumption in the next period was predicted, and the supercapacitor is charged and discharged in advance to meet the energy demand of uphill and the energy recovery capacity of downhill to avoid the high current charge and discharge of the battery. Subsequently, the improved EMS performance was simulated under the New York City Cycle (NYCC) driving conditions with additional slope driving conditions. The simulated results indicate that compared to the existing EMS, the proposed EMS based on slope information can effectively distribute the power demand between the power battery and the supercapacitor, can reduce the discharge current and the duration of high-power discharge, and has a 20.4% higher energy recovery efficiency, effectively increasing the cruising range. [ABSTRACT FROM AUTHOR]

Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm. [ABSTRACT FROM AUTHOR]

Since 2014, Taiwan has promoted a 1% annual electricity saving target to promote electrical efficiency efforts. As the industrial sector accounts for approximately 60% of the overall electricity consumption of Taiwan, this sector presents the greatest opportunity for improving Taiwan's overall energy efficiency. Here, the energy audit data of industrial energy users are analyzed via logistic regression to understand the factors impacting their likelihood of achieving the targeted 1% electricity saving. Of the variables under study, the number of employees and the rank of the energy administrator were significantly correlated with the likelihood of reaching the electricity saving target. Within the management structure of the factory, energy users with higher-ranking energy administrators are more likely to achieve the targeted 1% electricity saving. As it is impractical to rapidly increase the number of employees, higher-ranking employees, i.e., factory executives, should be appointed as energy administrators to improve users' electrical efficiency and thus reach the targeted 1% annual electricity saving. Based on the findings of our research, we put forward a point of view that in addition to the introduction of new technologies to improve energy efficiency, it can also be achieved through adjustments to the management structure of energy administration. [ABSTRACT FROM AUTHOR]

The increasing penetration of electrical vehicles (EVs), on the way to decarbonizing the transportation sector, presents several challenges and opportunities for the end users, the distribution grid, and the electricity markets. Uncontrollable EV charging may increase peak demand and impact the grid stability and reliability, especially in the case of non-interconnected microgrids such as the distribution grids of small islands. On the other hand, if EVs are considered as flexible loads and distributed storage, they may offer Vehicle to Grid (V2G) services and contribute to demand-side management through smart charging and discharging. In this work, we present a study on the penetration of EVs and the flexibility they may offer for services to the grid, using a genetic algorithm for optimum valley filling and peak shaving for the case of a non-interconnected island where the electricity demand is several times higher during the summer due to the influx of tourists. Test cases have been developed for various charging/discharging strategies and mobility patterns. Their results are discussed with respect to the current generating capacity of the island as well as the future case where part of the electricity demand will have to be met by renewable energy sources, such as photovoltaic plants, in order to minimize the island's carbon footprint. Higher EV penetration, in the range of 20–25%, is enabled through smart charging strategies and V2G services, especially for load profiles with a large difference between the peak and low demands. However, the EV penetration and available flexibility is subject to the mobility needs and limited by the population and the size of the road network of the island itself rather than the grid needs and constraints. Limitations and challenges concerning efficient V2G services on a non-interconnected microgrid are identified. The results will be used in the design of a smart charging controller linked to the microgrid's energy management system. [ABSTRACT FROM AUTHOR]

This research tackles effective and functional management practices in energy consumption using advanced technological solutions to mitigate unexpected events. This study specifically focuses on the recent COVID-19 pandemic that led to a reduction in energy consumption and therefore meddled with the project management's status quo for the energy sources. Moreover, this research analyzes the role of information and communication technology (ICT) solutions in energy management before and after the recent coronavirus pandemic turmoil. This study also examined the role and place of sustainable energy resources in the effective management of energy consumption during the COVID-19 pandemic. The main purpose of this research is to demonstrate how the recent coronavirus pandemic affected energy consumption and energy prices in many countries and how it paved the way for the new ICT in managing energy consumption. This study elaborated a model to show how these changes evolved in selected European Union (EU) Member States. It turns out that adjusted electricity consumption, as well as the average daily spot electricity prices for the baseload power in the EU countries, declined, and this trend is likely to continue in the next years to come. Due to the COVID-19 pandemic, substantial changes in the economy led to reduced office space usage, cutting commuting and business trips, and bringing commercial travel almost to a halt, further shifting the energy balance towards renewable energy sources (RES). Besides, it becomes apparent that ICT solutions used in maintaining and superintending the energy balance are gaining popularity and wide recognition worldwide and will help pave the way towards modern and smart high-renewables grids. [ABSTRACT FROM AUTHOR]

Predicting the energy needs of future water systems is important for coordinating long-term energy and water management plans, as both systems are interrelated. We use the case study of the Los Angeles City's Department of Water and Power (LADWP), located in a densely populated, environmentally progressive, and water-poor region, to highlight the trade-offs and tensions that can occur in balancing priorities related to reliable water supply, energy demand for water and greenhouse gas emissions. The city is on its path to achieving higher fractions of local water supplies through the expansion of conservation, water recycling and stormwater capture to replace supply from imported water. We analyze scenarios to simulate a set of future local water supply adoption pathways under average and dry weather conditions, across business as usual and decarbonized grid scenarios. Our results demonstrate that an aggressive local water supply expansion could impact the geospatial distribution of electricity demand for water services, which could place a greater burden on LADWP's electricity system over the next two decades, although the total energy consumed for the utility's water supply might not be significantly changed. A decomposition analysis of the major factors driving electricity demand suggests that in most scenarios, a structural change in LADWP's portfolio of water supply sources affects the electricity demanded for water more than increases in population or water conservation. [ABSTRACT FROM AUTHOR]

Society's concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become widespread during the last decade because of the distinct advantages they offer compared to the conventional ones. However, the increased penetration of EVs in the global transportation market has led increased electricity demands, which is expected to affect the operation of energy distribution systems. In the present paper, a demonstration about the effects of uncontrolled EVs charging in a case study low voltage (LV) network is demonstrated and a fuzzy energy management strategy for the coordination of EV charging in LV networks is presented, by including the distance of the EVs from the transformers in the fuzzy management systems for the first time. The Institute of Electrical and Electronics Engineers (IEEE) European Test Feeder is used as a case study low voltage distribution grid. In particular, the developed system configuration takes into consideration the architecture of the grid, the ampacities of the lines and the voltages at the system's buses. Moreover, electric vehicles are considered as agent-based models, which are characterized by the model of each EV, the state-of-charge of their batteries and the charging power. In particular, an investigation into the effects of uncontrolled charging is performed, in which two approaches are examined. The first approach investigates the maximum number of chargeable EVs in the case study network and how it is influenced by the grid's household loads. The second approach examines the number of network undervoltages and lines ampacity violations in a set of simulation scenarios. The results of the first approach show that the distance of the EVs from the networks substation affects the maximum number of chargeable EVs in a significant manner. Based on the observed results of the two approaches, a fuzzy management system is designed for the coordination of EV changing, which takes into account the distance from the EV charging points to the feeder substation, the state-of-charge of the EVs' batteries and the EVs' charging delay time. [ABSTRACT FROM AUTHOR]

Electricity demand is increasing, as a result of increasing consumers in the electricity market. By growing smart technologies such as smart grid and smart energy management systems, customers were given a chance to actively participate in demand response programs (DRPs), and reduce their electricity bills as a result. This study overviews the DRPs and their practices, along with home energy management systems (HEMS) and load management techniques. The paper provides brief literature on HEMS technologies and challenges. The paper is organized in a way to provide some technical information about DRPs and HEMS to help the reader understand different concepts about the smart grid, and be able to compare the essential concerns about the smart grid. The article includes a brief discussion about DRPs and their importance for the future of energy management systems. It is followed by brief literature about smart grids and HEMS, and a home energy management system strategy is also discussed in detail. The literature shows that storage devices have a huge impact on the efficiency and performance of energy management system strategies. [ABSTRACT FROM AUTHOR]

Efficient alternatives in energy production and consumption are constantly being investigated and conducted by increasingly strict policies. Buildings have a significant influence on electricity consumption, and their management may contribute to the sustainability of the electricity sector. Additionally, with growing incentives in the distributed generation (DG) and electric vehicle (EV) industries, it is believed that smart buildings (SBs) can play a key role in sustainability goals. In this work, an energy management system is developed to reduce the power demands of a residential building, considering the flexibility of the contracted power of each apartment. In order to balance the demand and supply, the electrical power provided by the external grid is supplemented by microgrids such as battery energy storage systems (BESS), EVs, and photovoltaic (PV) generation panels. Here, a mixed binary linear programming formulation (MBLP) is proposed to optimize the scheduling of the EVs charge and discharge processes and also those of BESS, in which the binary decision variables represent the charging and discharging of EVs/BESS in each period. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis are considered. The results point to a 65% reduction in peak load consumption supplied by an external power grid and a 28.4% reduction in electricity consumption costs. [ABSTRACT FROM AUTHOR]

The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios. [ABSTRACT FROM AUTHOR]