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Despite its high share in national energy demand, the tertiary sector is an undifferentiated residual in the energy balances of most countries. To develop effective policy measures for enhancing energy efficiency, policymakers need a sound understanding of how the energy demand is structured. To date, only a few studies assess energy demand in the tertiary sector quantitatively. This paper aims to close this gap by introducing an integrated model that transparently breaks down the energy demand according to statistical subsectors, energy carriers and end-uses. The model combines a technology-based bottom-up with a top-down approach and applies it to a case study on Germany based on survey data from companies. Model validity is analyzed using a set of indicators. The results for Germany show that the model is consistent with the national energy balance showing an aggregate deviation of only 1%. The results for 2019 indicate that electricity demand in Germany's tertiary sector is dominated by lighting (35%), information and communication technologies (33%) and the provision of mechanical energy (9%), while fuel demand is mainly due to space heating (78%), followed by mechanical energy (15%). [ABSTRACT FROM AUTHOR]

As the global average temperature has increased due to climate change, the use of air conditioning equipment for cooling homes has become more popular. Inverter equipment is advertised as a better energy option than systems with an on/off control; however, there is a lack of sufficient studies to prove this. This work aims to analyze and compare the electricity consumption associated with cooling equipment with an on/off control and inverter equipment. A heat transfer model coupled with energy balance for a room is developed and implemented in Python 3.12. The indoor temperature is controlled by simulating an on/off control and a PID control for the inverter system. Subsequently, the electricity consumption of the two systems is compared, and a sensitivity analysis is performed to determine which variables have the greatest impact on electricity consumption. The results show that the inverter equipment has lower electricity consumption compared to the equipment with the on/off control. However, the sensitivity analysis shows that the indoor temperature set point plays a more relevant role since a 15% variation in its value impacts electricity consumption by up to 77%. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective. [ABSTRACT FROM AUTHOR]

Electricity is fundamental to modern societies and will become even more so as its use expands through different technologies and population growth. Power generation is currently the largest source of carbon-dioxide (CO2) emissions globally, but it is also the sector that is leading the transition to net zero emissions through the rapid rise of renewables. The impacts of COVID-19 on the electricity sector led to a reduction in the demand for electricity, while at the same time, the current global energy crisis has placed the security and affordability of electricity at the top of the political agenda in many countries. In this way, the decrease in the demand for electricity, as well as its gradual recovery, makes it necessary to carry out energy planning that considers the adverse effects caused by global events with a high socioeconomic impact. In this article, the Low Emission Analysis Platform (LEAP) 2020 software has been used to determine the distribution of energy sources to 2050 for Mexico. The variables that lead to the possible profiles for 2050 are social, economic, and technological. The results correspond to a possible future based on official data from the National Electric System (SEN) of Mexico. The forecast for 2050 indicates that the electricity sector will have almost double the current installed capacity; however, emissions do not correspond to twice as much: they are practically 50% higher. [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%. [ABSTRACT FROM AUTHOR]

Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting. [ABSTRACT FROM AUTHOR]

The power generation capacity must be increased to accommodate population growth and address the lack of electricity access in rural areas. Traditional power plants in South Africa are unable to keep up with the growing demand for electricity. By strategically planning and building clusters of renewable energy sources like solar and wind, microgrid operators can provide a sustainable solution that boosts electricity supply while being cost-effective and environmentally friendly. Utilizing renewable energy can help alleviate strain on power plants by reducing peak demand in constrained distribution networks. The benefits of renewable energy include lower electricity expenses, enhanced system reliability, investment reallocation, and reduced environmental impact. These advantages will enhance the efficiency of the power system and contribute economic value to society. However, integrating solar power into the network infrastructure presents challenges such as fundamental changes in network structure, its intermittent nature due to unpredictability, and geographical constraints, which can complicate the task of grid operators in balancing electricity supply and demand within system limits while minimizing costs. The study employs Homer Pro 3.18.1 software to assess the economic costs and benefits of effectively integrating renewable technologies into the power grid. The aim is to evaluate the economic and technical feasibility of investing in renewable energy projects within the network. The research outcomes can guide power system operators, planners, and designers in leveraging solar energy to drive economic growth and industrial advancement, as well as assist independent power producers in making informed investment choices. [ABSTRACT FROM AUTHOR]

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. [ABSTRACT FROM AUTHOR]

This study investigates how differences in energy-related properties of cities influence the composition of a cost-efficient urban energy system, assuming electrification of the transport and industry sectors and zero-emission of CO2. These differences are evaluated for two scenarios regarding the capacities of the modeled cities to import electricity. A linear optimization model that encompasses the electricity, heating, industry, and transport sectors, using measured data from six cities in Sweden, is applied. Results show that when strict constraints on electricity imports are enforced, cities with a lower ratio of annual electricity demand for heat encourage the implementation of power-to-heat solutions in the heating sector. This study also reveals that under such stringent electricity import conditions, cities with a high level of flexibility in electricity demand favor a combination of batteries and solar photovoltaics as opposed to biomass-based electricity production. Conversely, when electricity importation is less restricted and biomass prices surpass 20 EUR/MWh, local electricity generation is outcompeted by imports, and large-scale heat pumps working in tandem with thermal energy storage dominate the heating sector in all modeled cities. This assertion holds true when the maximum electricity import capacity is utilized up to 5000 h annually. [ABSTRACT FROM AUTHOR]

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]

In the West African Monetary and Economic Union (UEMOA), information on the characteristics of the users and patterns of electricity end-uses remains hard to find. This study aims to contribute to reducing the gap in research on domestic electricity consumption in the region by unveiling the ownership rates, patterns of use and electricity consumption of domestic appliances in urban households through a city-wide survey. Three categories of urban users were investigated including high, medium and low consumers. Findings demonstrated various ownership rates for appliances, ranging from 100% for lighting fixtures to 0% for washing machines depending on user category. Domestic electricity demonstrated patterns consisting of three peak demand periods, with the main ones occurring in the evening (19:00 to 20:00) and the night (22:00). Other demand characteristics include an average daily electricity use ranging from 0.50 to 6.42 kWh per household, a maximum power demand of between 0.19 and 0.70 kW and a daily load factor between 35 and 58%. Finally, the appliances contributing the most to domestic electricity use include air-conditioners, fans, fridges and freezers, televisions and lighting fixtures, with contributions differing from one category of user to another. Policy implications including review of the appliances' importations framework and policies, and incentives for purchasing efficient appliances, design of more tailored policies, considering the different backgrounds of the users, education enhancement on energy behaviours for increasing energy efficiency/conservation, and implementation of DSM programs including load levelling, load shifting and load reducing depending on the type of appliance for energy conservation in the domestic buildings were derived. Overall, a large range of stakeholders of the electricity sector, not only in the West African Economic and Monetary Union (UEMOA), but also in other regions and countries sharing common characteristics should be interested in the results of this study. [ABSTRACT FROM AUTHOR]

Campus buildings often face issues with high energy consumption, low efficiency, and significant carbon emissions, making the creation of a green, low-carbon campus urgent. Utilizing solar photovoltaics on rooftops can provide an effective power solution to address high energy consumption. This study focuses on a university campus, employing the DeST energy consumption simulation software to model the HVAC systems, electrical devices, and hot water loads of five typical buildings. It combines this with calculations of available rooftop areas to assess the potential for rooftop solar photovoltaics. The results confirm varying annual electricity consumption among the different buildings, which directly correlates with building size and operational schedules. Among the five building types, sports facilities and academic buildings have relatively high rooftop photovoltaic self-sufficiency rates, exceeding 60%, while the library has the lowest, under 20%. The entire university campus has an annual rooftop photovoltaic self-sufficiency rate of 35%, significantly addressing the issue of high energy consumption in university campuses. This research provides a theoretical basis for implementing rooftop photovoltaic systems to achieve campus energy savings. [ABSTRACT FROM AUTHOR]

In order to reduce greenhouse gas emissions associated with power generation, the replacement of fossil fuels with renewables must be accompanied by the availability of dispatchable sources needed to balance electricity demand and production. Combined cycle (CC) power plants adopting post-combustion capture (PCC) can serve this purpose, ensuring near-zero CO2 emissions at the stack, as well as high efficiency and load flexibility. In particular, the chemical absorption process is the most established approach for industrial-scale applications, although widespread implementation is lacking. In this study, different natural gas combined cycle (NGCC) configurations were modeled to estimate the burden of retrofitting the capture process to existing power plants on thermodynamic performance. Simulations under steady-state conditions covered the widest possible load range, depending on the gas turbine (GT) model. Attention was paid to the net power loss and net efficiency penalty attributable to PCC. The former can be mitigated by lowering the GT air–fuel ratio to increase the CO2 concentration (XCO2) in the exhaust, thus decreasing the regeneration energy. The latter is reduced when the topping cycle is more efficient than the bottoming cycle for a given GT load. This is likely to be the case in the less-complex heat recovery units. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to forecast electrical demand for the Chilean Electrical Power System considering a national, regional, district and bus spatial disaggregation. The methodology developed was based on different kinds of econometric models and end-use models to represent the massification of low carbon emission technologies such as electromobility, electric heating, electric water heating, and distributed generation. In addition, the methodology developed allows for the projection of the electric demand considering different kinds of clients as regulated and non-regulated clients, and different economic sectors. The model was applied to forecast the long-term electricity demand in Chile for the period 2022–2042 for 207 districts and 474 buses. The results include projections under the base case and low carbon scenarios, highlighting the significant influence of new technologies on future demand. [ABSTRACT FROM AUTHOR]

With the development of the economy and society, the importance of a secure and stable electricity supply continues to increase. However, the power grid is facing the test of excess installed capacity, the waste of renewable energy, and a low comprehensive utilization rate. This problem stems from the inconsistent peak–valley differences between power production and consumption, and the lack of clear electricity price signals, which disrupts the safe and stable operation of the power market. This paper combines the interactive transactions among clean energy power generation companies, users, and energy storage, explores how the system optimization model can be reflected in the power market through regulatory measures, and formulates the optimal output scheme of the system under the constraints of clean energy power generation forecast data, user base load forecast data, demand-side resource regulation ability, and energy storage system regulation ability to achieve the goals of comprehensive clean energy power consumption and minimum cost for users. A comprehensive analysis of the proposed model was conducted using actual data from a certain province in China, the results show that the consumption of clean energy will increase by 3% to full consumption and the total cost of users will be 32% lower than that of time-of-use (TOU) power prices, which proves the potential of the proposed joint optimization model in absorbing clean energy and the effectiveness of the market mechanism. [ABSTRACT FROM AUTHOR]

In the context of power system decarbonization, the demand-side strategy for increasing the share of renewable energy is studied for two constrained energy systems. This strategy, which is currently widely suggested in policies on the energy transition, would impact consumer behavior. Despite the importance of studying the latter, the focus here is on decisions regarding the type, location, and timeframe of implementing the related measures. As such, solutions must be assessed in terms of cost and feasibility, technological learning, and by considering geographical and environmental constraints. Based on techno-economic optimization, in this paper we analyze the evolution of the power system and elaborate plausible long-term trajectories in the energy systems of two European islands. The case studies, Procida in Italy and Hinnøya in Norway, are both electrically connected to the mainland by submarine cables and present issues in their power systems, which are here understood as relatively isolated power systems. Renewable energy integration is encouraged by legislative measures in Italy. Although not modeled here, they serve as a backbone for the assumptions of increasing these investments. For Procida, rooftop photovoltaics (PV) coupled with energy storage are integrated in the residential, public, and tertiary sectors. A price-based strategy is also applied reflecting the Italian electricity tariff structure. At a certain price difference between peak and off-peak, the electricity supply mix changes, favoring storage technologies and hence decreasing imports by up to 10% during peak times in the year 2050. In Norway, renewable energy resources are abundant. The analysis for Hinnøya showcases possible cross-sectoral flexibilities through electrification, leading to decarbonization. By fine-tuning electric vehicle charging tactics and leveraging Norway's electricity pricing model, excess electricity demand peaks can be averted. The conclusions of this double-prospective study provide a comparative analysis that presents the lessons learnt and makes replicability recommendations for other territories. [ABSTRACT FROM AUTHOR]

The use of fossil fuel-based energy systems that provide heat and electricity to a building has adverse environmental impacts. These impacts can be mitigated, to a certain extent, through the incorporation of renewable energy sources (RES). The primary objective of this study was to conduct an environmental assessment of the performance of energy systems in existing public facilities located in Poland. Based on the findings, we proposed and implemented changes to these systems and validated the environmental impact of the RES systems used. SimaPro 8.1 software and the Ecoinvent 3.0 database were employed for the analysis, which entailed an environmental assessment of six public facilities located in Poland. The installation of RES resulted in an average 27% reduction in electricity consumption from the national electricity grid. This reduction was observed to be the least in the hospital and the most in the religious building. This was reflected in the environmental assessment of heating systems. The implementation of RES reduced the environmental impact of the religious building by an average of 20%. Concurrently, the CO2 emissions decreased by 35%, SO2 by 44%, and PM10 by 42%. Significant investments and the installation of advanced RES will not prevent the occurrence of unintentional environmental consequences unless the demand for electricity and thermal energy is reduced. The use of RES in the analyzed buildings and the associated avoided emissions do not entirely offset the negative emissions resulting from the utilization of other (conventional) energy sources in the analyzed energy systems of public buildings. Consequently, the analyzed facilities collectively exert a detrimental impact on the environment. [ABSTRACT FROM AUTHOR]

This work presents an economic analysis of the use of electricity storage in PV installations, based on previously adopted assumptions, i.e., the type and location of the tested facility and comparative variants, divided into the share of the storage in the installation, and the billing system. The work takes into account the share of the energy shield and assumes a consumption limit of 2000 kWh. The cost of building the installation is based on July 2023 prices. The work assumes potential directions of changes in electricity prices, based on which the degree of investment profitability for a given price situation is determined. Depending on the adopted change in the direction of electricity prices, with a low price increase rate, for installations in the new billing system (net-billing), the optimal choice is an installation without energy storage with a power exceeding the actual energy demand. Assuming a high increase in electricity prices, the optimal choice is an installation with energy storage with an installation capacity exceeding the actual demand. For installations billed using the net-metering system, the optimal choice is an installation without storage with an appropriately selected installation power. This article shows how much you can gain after installing a PV installation and not only what costs must be incurred to complete the investment. Profit analysis will enable a more complete assessment of the profitability of investing in PV panels (with or without energy storage). It describes the verification of the profitability of a PV installation for a standard user depending on various types of settlements with the electricity supplier and the lack or installation of an energy-storage facility. [ABSTRACT FROM AUTHOR]

In the context of the German Energiewende, the current government intends to install six million heat pumps by 2030. Replacing gas heating by power has significant implications on the infrastructure. One of the biggest advantages of using gas is the existing storage portfolio. It has not been clarified yet how power demand should be structured on an annual level—especially since power storage is already a problem and solar power is widely promoted to fuel heat pumps, despite having an inverse profile. In this article, three different solutions, namely, hydrogen, batteries, and carbon capture and storage, are discussed with respect to resources, energy, and financial demand. It shows that relying solely on batteries or hydrogen is not solving the structuring problem. A combination of all existing technologies (including fossil fuels) is required to structure the newly generated electricity demand. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

Residential consumption represents one of the most important percentages of total electricity consumption. A considerable number of household appliances consume energy even when they are not in operation, i.e., they are in the so-called standby state, thus producing additional costs, which become significant over time. In this context, one method to solve this problem is to develop a smart system capable of severing the power connection to devices in standby mode, thereby conserving energy and reducing the energy costs. The first step in the design of this system consists of the identification and accurate measurement of the standby state, which was carried out for three of the most common household appliances. Then, by using an ESP32 microcontroller, a system was designed to manage the operation of a relay module based on the current consumption of the connected equipment. Control over the system was achieved through a web application that works across all devices equipped with a web browser, offering functionalities to adjust current value time delays and to manually switch the system on or off. Finally, the deployment of this system across the three appliances studied led to a reduction in the energy consumption in standby mode of 26.68 kWh per month. [ABSTRACT FROM AUTHOR]

Australia, like many other countries around the world, is undergoing a transition toward net zero emissions. It requires changes and development in many sectors, which not only bring benefits but also challenges. The rapid growth in renewable energy sources (RESs) is necessary to decarbonise electricity generation but negatively affects grid stability. Residential buildings also contribute to this issue through specific load profiles and the high penetration of rooftop photovoltaic (PV) installations. Maintaining grid balance will be crucial for further emissions reductions. One of the potential solutions can be the replacement of conventional heating and cooling systems in houses with solutions capable of storing energy and shifting the electrical load. As presented in this paper, heat pumps and hydronic systems can significantly improve the electrical load of a typical South Australian household when they are controlled by algorithms reacting to the current grid conditions and household-generated electricity compared to conventional solutions. TRNSYS 18 simulations of air source and ground source heat pump systems with smart control based on measured electricity consumption and domestic hot water usage data showed the possibility of total energy consumption reduction, shifting the load from peak periods towards periods of excessive RES generation and increasing self-consumption of rooftop PV electricity. These improvements reduce the amount of emissions generated by such a household and allow for further development of other sectors. [ABSTRACT FROM AUTHOR]

Overhead lines are used to transmit electricity from where it is generated to the receiving stations. The correct design of an overhead line affects public safety, because it should ensure the required clearances between conductors and the ground and objects located in the space under the overhead line. The temperature of conductors in overhead lines depends on the load current and weather conditions, and affects the sag and tension of the conductors. Calculations of sags and tensions of overhead conductors can be performed using simplified calculation methods that do not consider insulator sets. In some situations, this approach may cause calculation errors. This article discusses algorithms for calculating overhead conductor tensions and sags in the tensioning sections of high-voltage overhead lines, accounting for and excluding insulator sets. The analysis is carried out for different lengths of tensioning sections and various thermal and mechanical states of the conductors. [ABSTRACT FROM AUTHOR]

This paper examines how Australian residential load profiles may evolve in the short to medium term future. These profiles can be used to support simulation studies of the future Australian network within an environment that is transitioning to renewable energy and broader use of electricity as a tool for decarbonisation. The daily profiles rely heavily on the Australian Energy Market Operator (AEMO) forecasts for future annual energy usage. The period from 2024 to 2050 will be transformational. In the residential networks, two secular trends are particularly important in expanding residential generation and electrification. New daily load profiles have been constructed using historical Australian profiles and adding additional components for solar generation, battery operation and electrification activities. The entire aggregated residential network is expected to have reverse midday power flow on any average day from 2024 onwards due to the rapid increase in electric vehicle (EV) usage. The domestic energy demand forecasting methodology presented in this work related to Australia can easily be adopted to carry out similar forecasting for any country of the world. [ABSTRACT FROM AUTHOR]

The need for renewable energy is regarded as a major component in the move towards achieving sustainable development. Using a large sample of 177 countries over the period 1990 to 2020, this research explores the impact of the most significant drivers of renewable energy. Findings from this work contribute to the literature by identifying the most significant drivers of renewable energy deployment and their different responses in developed and developing economies. Empirical results suggest that GDP, oil price, access to electricity, and CO2 and methane emissions are significant determinants of renewables both as a share in energy consumption and as a share in electricity production. Additionally, trade is found to be a significant driver for the share of renewables in total energy consumption but not for the share of renewables in the electricity production model. Finally, our findings indicate that the factors influencing the development of renewables vary significantly between developed and developing countries, necessitating distinct approaches for each group of countries. These results can play a significant role from a policy perspective in designing and implementing specific policies to increase renewable energy deployment. [ABSTRACT FROM AUTHOR]

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone's electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze the economical suitability of numerous on-site renewable electricity generation technologies which were intended to be used in a recently built industrial facility designed and utilized as a warehouse. The facility was located in the vicinity of Riga, Latvia. Data were collected and calculations were performed within the scope of the project "Mitigating Energy Poverty through Innovative Solutions" as part of several planned activities to address the broad spectrum of energy poverty and self-reliance issues in both the residential sector and small-scale industrial facilities. During the project, evaluations of various renewable energy technologies, including PV installations, wind energy installations, battery storage solutions, and hybrid technologies, were carried out. The aim of these evaluations was to develop an electricity production–consumption model for efficient and cost-effective energy use and to reduce greenhouse gas emissions from the test facility. A model was created and subsequent research scenarios were developed based on a payback period instead of the net present value criterion. The project was carried out over several steps to develop a calculation methodology. The open access databases of energy resource providers were used to evaluate statistical data and make forecasts for the analysis of the electricity consumption of companies. MATLAB/Simulink 23/2 was used for the data analysis, and the H-TEC method was employed. This made it possible to modulate the required production capacity as the model allowed for the addition of new modules to modules already installed. The project results proved that despite high initial investment costs, renewable energy sources and efficient storage systems can provide cost-effective solutions and reduce dependence on fossil fuels in the long term. [ABSTRACT FROM AUTHOR]

An air-based photovoltaic thermal collector (PVTC) is a system that generates both electricity and heat using air flowing over a photovoltaic (PV) module. This system offers the advantage of easy maintenance; however, it suffers from lower thermal efficiency compared to other PVTCs, mostly owing to the low heat capacity of air. Thus, this study introduces a novel PVTC incorporating dual ducts and semicircular turbulators, which were experimentally evaluated under actual weather conditions in the Republic of Korea. The proposed PVTC was compared with two other types of PVTC: one is a single-duct PVTC with semicircular turbulators, and the other is a dual-duct PVTC without turbulators. The results showed that the thermal efficiency of the proposed PVTC increased by approximately 88.7% compared to the single-duct PVTC with a turbulator and by 9.3% compared to the dual-duct PVTC without a turbulator. The electrical efficiency showed a slight decrease of about 7.2% compared to the single-duct PVTC but an increase of 1.4% compared to the dual-duct PVTC without a turbulator. Overall, the total efficiency of the proposed PVTC increased by 54.2% and 7.7% compared to the single-duct PVTC and the dual-duct PVTC without a turbulator, respectively. These experimental results demonstrate that attaching dual ducts and semicircular turbulators to an existing PVTC increases the daily thermal energy output, which ultimately enhances the total daily energy output. [ABSTRACT FROM AUTHOR]

The ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings. [ABSTRACT FROM AUTHOR]

System health monitoring (SHM) of a ball screw laboratory system using an embedded real-time platform based on Field-Programmable Gate Array (FPGA) technology was developed. The ball screw condition assessment algorithms based on machine learning approaches implemented on multiple platforms were compared and evaluated. Studies on electric power consumption during the processing of the proposed structure of a neural network, implementing SHM, were carried out for three hardware platforms: computer, Raspberry Pi 4B, and Kria KV260. It was found that the average electrical power consumed during calculations is the lowest for the Kria platform using the FPGA system. However, the best ratio of the average power consumption to the accuracy of the neural network was obtained for the Raspberry Pi 4B. The concept of an efficient and energy-saving hardware platform that enables monitoring and analysis of the operation of the selected dynamic system was proposed. It allows for easy integration of many software environments (e.g., MATLAB and Python) with the System-on-a-Chip (SoC) platform containing an FPGA and a CPU. [ABSTRACT FROM AUTHOR]

This study evaluates a hybrid heating system in a single-family building in northeastern Poland, which has a temperate continental climate. The analysis covers two heating seasons in 2021/2022 and 2022/2023. The hybrid heating system includes an air heat pump HPA–08 CS Plus with a heating power of 8.2 kW (AHP), a condensing gas boiler VC146/5–5 with a power of 14 kW (GB–Condens.), and a solid fuel boiler with a power of 11 kW for central heating. Additionally, hot water is heated by a Basic 270 (DHW's AHP) air–water heat pump with a power of 2 kW, utilizing a tank with a capacity of 270 dm3 equipped with two heating coils. The building's average electricity consumption is around 5400 kWh/year. A 4.96 kWp photovoltaic installation is installed on the building's roof at a 40° angle towards the south to supplement the hybrid system. The study aims to assess whether the PV installation can adequately cover the energy needs of the hybrid heat source for heating and hot water. Furthermore, the study calculates the emission of pollutants (CO2, SOx, NOx, CO, and PM10) into the atmosphere. The total annual electricity production from PV installations was 5444.9 kWh in 2021/2022 and 5684.8 kWh in 2022/2023. The excess electricity was stored in the PGE power grid as per the Prosumer settlement rules. The installed PV installation is sufficient to power the following devices annually: AHP, DHW's AHP, and GB–Condens. However, the daily electricity production from the PV installation is not enough to cover the energy needs of the heat pump for heating during the cold months in Poland (I–III, XI–XII). It can meet the power needs of a PC all year round and can also be stored during the summer months, for example, in energy warehouses or by directly storing it in the PGE power grid. The use of the PV installation resulted in an average reduction in pollutant emissions into the atmosphere: CO2—94.1%, SOx—91.8%, NOx—95.6%, CO—9.7%, and PM10—32.1%. [ABSTRACT FROM AUTHOR]

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra "discriminator" named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two "discriminators" correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model's feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual "discriminators". Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy. [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]

The general development of technology and universal access means that gastronomy furnishings can be found in every corner of the world. Therefore, it is important to develop these devices and the machines that constitute them. We are talking about refrigeration systems located inside gastronomy furnishings. The R404A refrigerant, popular in recent years, is being withdrawn from use due to its harmful impact on the environment. Modern synthetic refrigerants or natural refrigerants can be used as alternative substances. In modern solutions, it is expected that devices and all cooperating elements have the lowest possible harmful impact on the environment and the user while, at the same time, having the highest possible energy efficiency. First, tests were carried out with the R404A refrigerant. Then, the working medium was replaced without changing any element of the refrigeration system with the modern R455A refrigerant. The system was tested in terms of the operating parameters achieved and in terms of electricity consumption. It was found that there is an alternative R455A refrigerant operating in the refrigeration system of catering furnishings, which provides the system with an average of 34% better energy efficiency than the reference refrigerant R404A. It was also found that the time needed to achieve the set working conditions decreased. An alternative refrigerant allows a refrigeration system to be built based on components available on the market or one that can be used as a direct replacement for the old refrigerant. [ABSTRACT FROM AUTHOR]

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts. [ABSTRACT FROM AUTHOR]

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. [ABSTRACT FROM AUTHOR]

The demand for different energy goods and services is a fundamental component in a country's economic structure for development. Understanding it is vital in designing economic policies, such as taxes, that can improve the welfare of the population. A comprehension of the distributional effects of elasticities and the application of them to simulate household responses to price changes, as well as a calculation of the welfare impacts on poor and rich households in Mexico, should inform policy design. This paper uses the Household Income and Expenditure Survey (ENIGH) from 1996 to 2018 to estimate the demand of Mexican households for fuels, specifically electricity, liquefied petroleum gas, and gasoline. A Quasi Ideal Quadratic Demand System (QUAIDS) is employed to analyse the effects of removing energy subsidies and introducing a carbon tax. The results indicate that welfare losses would be regressive concerning electricity price increases, while changes in gasoline prices would be progressive. Redistributing the tax revenues accrued by removing energy subsidies and imposing the carbon tax would have more progressive effects on the economy of Mexican households, with welfare gains of up to 350% for the poorest households in the case of electricity consumption taxes. [ABSTRACT FROM AUTHOR]

Current developments in vehicles have generated great interest in the research and optimization of heating, ventilation, and air conditioning (HVAC) systems as a factor to reduce fuel consumption. One of the key trends for finding solutions is the intensive development of electric transport and, consequently, additional requirements for reducing energy consumption and modifying climate systems. Of particular interest is the optimal functioning of comfort and life support systems during air recirculation, i.e., when there is a complete or partial absence of outside air supply, in particular to reduce energy consumption or when the environment is polluted. This work examines numerical models of airfields (temperature, speed, and humidity) and also focuses on the concentration of carbon dioxide and oxygen in the cabin, which is a critical factor for ensuring the health of the driver and passengers. To build a mathematical model, the Navier–Stokes equations with energy, continuity, and diffusion equations are used to simulate the diffusion of gases and air humidity. In the Ansys Fluent finite volume analysis package, the model is solved numerically using averaged RANS equations and k-ω turbulence models. The cabin of a mainline locomotive with two drivers, taking into account their breathing, is used as a transport model. The problem was solved in a nonstationary formulation for the design scenario of summer and winter, the time of stabilization of the fields was found, and graphs were constructed for different points in time. A comparative analysis of the uniformity of fields along the height of the cabin was carried out with different locations of deflectors, and optimal configurations were found. Energy efficiency values of the climate system operation in recirculation operating modes were obtained. A qualitative assessment of the driver's blowing directions under different circulation and recirculation modes is given from the point of view of the concentration of carbon dioxide in the breathing area. The proposed solution makes it possible to reduce electricity consumption from 3.1 kW to 0.6 kW and in winter mode from 11.6 kW to 3.9 kW and save up to 1.5 L/h of fuel. The conducted research can be used to develop modern energy-efficient and safe systems for providing comfortable climate conditions for drivers and passengers of various types of transport. [ABSTRACT FROM AUTHOR]

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side. [ABSTRACT FROM AUTHOR]

The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge is devising incentive mechanisms that reward distribution utilities for their operational and investment choices, aiming to mitigate or decrease electricity non-payment rates and avoid tariff increases for regular customers. Despite adhering to the principles of incentive regulation, the Brazilian Electricity Regulatory Agency (ANEEL) uses a methodological approach to define regulatory targets for electricity defaults tied to econometric models developed to determine targets to combat electricity non-technical losses (NTLs). This methodology has been widely criticized by electricity distribution utilities and academics because it includes many ad hoc steps and fails to consider the components that capture the specificities and heterogeneity of distribution utilities. This study proposes a fuzzy inference-based model for defining regulatory default targets built independently of the current methodological approach adopted by ANEEL and aligned with the principles of incentive regulation. An empirical study focusing on the residential class of electricity consumption demonstrated that it is possible to adopt a specific methodology for determining regulatory default targets and that the fuzzy inference approach can meet the necessary premises to ensure that the principles of incentive regulation and the establishment of regulatory targets are consistent with the reality of each electricity distribution utility. [ABSTRACT FROM AUTHOR]

According to the recent revision of the Greek National Energy and Climate Plan, the country sets out to accomplish an ambitious target concerning the integration of renewables in the local electricity mix during the ongoing decade, at the levels of 80% by 2030. This implies the need to more than double the existing wind and PV capacity at the national level, which in turn introduces numerous challenges. Amongst them, spatial planning for new RES installations seems to be the most demanding, with the adoption of novel technological solutions in the field of RES potentially holding a key role. New technologies, like offshore wind and floating solar, are gradually gaining maturity and may offer such an alternative, challenged at the same time however by the need to entail minimum disruption for local ecosystems. To that end, we currently assess the theoretical potential of floating PVs for lentic water systems of the Greek mainland. We do so by looking into 53 different lentic water systems across the Greek territory that meet the constraint of 1 km2 minimum surface area, and we proceed with the estimation of the relevant floating PV capacity per system under the application of a minimal intervention approach, assuming PV coverage of 1% over the total lentic water system area. In this context, our findings indicate a maximum, aggregate theoretical capacity that could exceed 2 GWp at the national level, with the respective annual energy yield reaching approximately 4 TWh or, equivalently, >6% of the country's anticipated annual electricity consumption in 2030. Finally, our results extend further, offering a regional level analysis and a set of policy directions and considerations on the development of floating solar in Greece, while also designating the energy merits of floating PVs against similar, land-based installations. [ABSTRACT FROM AUTHOR]

In the wake of the dual-carbon objective, the call for low-carbon attributes in integrated energy systems is ascending, with an amplified imperative to integrate wind and solar power efficiently. This study introduces an advanced low-carbon optimization framework for integrated energy systems, incorporating a sophisticated time-differentiated carbon accounting mechanism attentive to consumer emissions. A nuanced carbon accounting model is crafted to assess consumer emissions with greater accuracy. Predicated on these emissions, a refined low-carbon demand response model is articulated, factoring in the influence of carbon emission factors pertinent to electricity and heat procurement on user conduct. This model integrates the consideration of heat reclaimed from methanation processes, which in turn informs the carbon emission factors associated with purchased heat, and evaluates the subsequent optimization impact on the system. The proposed model is designed to curtail the system's operational expenditures and is operationalized via the CPLEX solver. Through the establishment of various scenarios for evaluative comparison, the model is corroborated to substantially augment the system's proficiency in assimilating wind and solar energy, markedly curtail carbon emissions, and facilitate a sustainable and cost-efficient operation of the integrated energy system. [ABSTRACT FROM AUTHOR]

This paper takes a low-energy building in Changchun, China, as an object to test and study the characteristics of two heating modes, AC/DC (Alternative current/Direct current) switching and AC/DC synthesis, from the perspectives of temperature change, irradiation intensity, power generation, electricity consumption, etc. Firstly, the experimental research was conducted under two heating cable modes by establishing mathematical models and a test rig, and it was found that the photoelectric conversion efficiency on sunny, cloudy, and overcast days was 18%, 14.5%, and 12%, respectively. A simulation model was established by TRNSYS to run an ultra-low-energy building throughout the year. It was found that the highest and lowest monthly power generation occurred in February and July, respectively. The annual power generation of the system was 6614 kWh, and the heating season power generation was 3293.42 kWh. In the current research, the DC electricity consumption was slightly higher than the AC electricity consumption. Under conditions of similar radiation intensity and power generation, the indoor temperature of the AC/DC synthesis cable heating mode were 1.38% higher than the AC/DC switching heating able mode, and the electricity consumption were 10.9% and 4.76% higher, respectively, than those of the AC switching heating cable mode. This is of great significance for clean-energy heating, energy savings, and emissions reduction in northern China. [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]

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations. [ABSTRACT FROM AUTHOR]

This study investigated the impact of information and communication technology (ICT) on electricity intensity, incorporating electricity prices, financial development, and population growth in Korea from 1990 to 2020, using the ARDL (autoregressive distributed lag) model. Three cases were considered, each relating to a different ICT proxy: Internet use, mobile cellular phone use, and exports of ICT-related products. The results varied depending on the proxy used to represent ICT. An increase in mobile cellular phone use leads to an increase in electricity intensity in the long run; however, the short-run effects of this change are unclear. An increase in Internet use also leads to an increase in electricity intensity in the long run but induces a decrease in electricity intensity in the short run. Increments in the exports of ICT-related products lead to an increase in electricity intensity in the short run; however, this effect is negligible in the long run. Electricity prices do not affect electricity intensity in all cases, and financial development reduces the intensity of electricity in the cases of the use of both mobile cellular phones and the Internet as proxies for ICT, whereas population growth increases electricity intensity in all cases. [ABSTRACT FROM AUTHOR]

Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a novel model based on Lasso regression, an ARIMA model, and a BPNN is proposed. Lasso regression is used to screen the key factors affecting carbon emissions, and the ARIMA model is used to extract the linear components of the carbon emission sequences, while the BPNN is used to predict the residuals of the ARIMA model. The final result is the sum of that from the ARIMA model and the BPNN. The carbon peak, carbon neutralization time, and emissions were analyzed under different scenarios. Taking Suzhou City as an example, the results show that the electricity consumption of the whole population is one of the key drivers of carbon emissions; the carbon emission prediction accuracy and stability of the ARIMA-BPNN combined model are better than those of the single model, which improves the reliability as well as the accuracy of the model's prediction. However, under the constraints of the current policies, the goal of achieving carbon peaking by 2030 in Suzhou City may not be realized as scheduled. This novel carbon emission prediction model built was validated to provide a scientific basis for low-carbon urban development. This study presents an important reference value for predicting carbon emissions and formulating emission reduction measures in other cities. [ABSTRACT FROM AUTHOR]