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Journal articles on the topic 'Aggregate residential load'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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1

Zhou, Xiao, Jing Shi, Yuejin Tang, Yuanyuan Li, Shujian Li, and Kang Gong. "Aggregate Control Strategy for Thermostatically Controlled Loads with Demand Response." Energies 12, no. 4 (2019): 683. http://dx.doi.org/10.3390/en12040683.

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The improvement of intelligent appliances provides the basis for the demand response (DR) of residential loads. Thermostatically controlled loads (TCLs) are one of the most important DR resources and are characterized by a large load and a high degree of control. Due to its distribution characteristic, the aggregation of TCLs and their control are key issues in implementing the load control for the DR. In this study, we focus on air conditioning loads as an example of TCLs and propose a simple and transferable aggregate model by establishing a virtual house model, which accurately captures the
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Al-Rawi, Azhar M. "A Realistic Aggregate Load Representation for A Distribution Substation in Baghdad Network." Journal of Engineering 24, no. 2 (2018): 100–117. http://dx.doi.org/10.31026/j.eng.2018.02.07.

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Electrical distribution system loads are permanently not fixed and alter in value and nature with time. Therefore, accurate consumer load data and models are required for performing system planning, system operation, and analysis studies. Moreover, realistic consumer load data are vital for load management, services, and billing purposes. In this work, a realistic aggregate electric load model is developed and proposed for a sample operative substation in Baghdad distribution network. The model involves aggregation of hundreds of thousands of individual components devices such as motors, appli
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Huang, Hui, Yun Gao, Song Wang, et al. "Aggregate scheduling potential evaluation of large-scale air conditioning loads." Journal of Physics: Conference Series 2757, no. 1 (2024): 012012. http://dx.doi.org/10.1088/1742-6596/2757/1/012012.

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Abstract As an adjustable load, the air conditioning (AC) load is a high-quality resource for power system operation regulation. However, due to the large number of AC loads and the heterogeneity among individual users, the scheduling potential of AC loads is difficult to accurately calculate. An aggregate scheduling potential evaluation of large-scale AC loads is proposed. Firstly, according to the operation characteristics of ACs, the aggregation models of central ACs and household split-type ACs are constructed by using the equivalent thermal parameter model and Monte Carlo sampling method
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Mohammadabadi, Abolfazl, Igor Sartori, and Laurent Georges. "Validation of the Energy Demand Load Profile Estimator “PROFet” for Trondheim Non-residential Buildings." E3S Web of Conferences 562 (2024): 11002. http://dx.doi.org/10.1051/e3sconf/202456211002.

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Accurate long-term forecasts of aggregate energy load profiles are crucial for effective energy system planning at regional and national scales. This study aims to validate PROFet, a flexible load profile modeling tool. PROFet forecasts weather-dependent heating and electrical load profiles at an hourly resolution for both residential and non-residential buildings connected to district heating systems. Given that the tool’s accuracy for residential buildings has been demonstrated in previous studies, further validation is needed for non-residential buildings. To achieve this, our study evaluat
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Djokic, Sasa Z., and Igor Papic. "Smart Grid Implementation of Demand Side Management and Micro-Generation." International Journal of Energy Optimization and Engineering 1, no. 2 (2012): 1–19. http://dx.doi.org/10.4018/ijeoe.2012040101.

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This paper analyses the influence and effects of demand side management (DSM) and micro-generation (MG) on the operation of future “smart grids.” Using the residential load sector with PV and wind-based MG as an example, the paper introduces a general methodology allowing to identify demand-manageable portion of the load in the aggregate demand, as well as to fully correlate variable power outputs of MG with the changes in load demands, including specific DSM actions and schemes. The presented analysis is illustrated using a detailed model of a typical UK LV/MV residential network.
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Nashrullah, Erwin, and Abdul Halim. "Polynomial Load Model Development for Analysing Residential Electric Energy Use Behaviour." MATEC Web of Conferences 218 (2018): 01007. http://dx.doi.org/10.1051/matecconf/201821801007.

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Analysing and simulating the dynamic behaviour of home power system as a part of community-based energy system needs load model of either aggregate or dis-aggregate power use. Moreover, in the context of home energy efficiency, development of specific and accurate residential load model can help system designer to develop a tool for reducing energy consumption effectively. In this paper, a new method for developing two types of residential polynomial load model is presented. In the research, computation technique of model parameters is provided based on median filter and least square estimatio
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Afzaal, Muhammad Umar, Intisar Ali Sajjad, Muhammad Faisal Nadeem Khan, et al. "Inter-temporal characterization of aggregate residential demand based on Weibull distribution and generalized regression neural networks for scenario generations." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 4491–503. http://dx.doi.org/10.3233/jifs-200462.

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The characterization of electrical demand patterns for aggregated customers is considered as an important aspect for system operators or electrical load aggregators to analyze their behavior. The variation in electrical demand among two consecutive time intervals is dependent on various factors such as, lifestyle of customers, weather conditions, type and time of use of appliances and ambient temperature. This paper proposes an improved methodology for probabilistic characterization of aggregate demand while considering different demand aggregation levels and averaging time step durations. At
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Pires, Vitor Fernão, Armando Cordeiro, Tito G. Amaral, João F. Martins, and Ilhami Colak. "The Impact of Power Definitions on the Disaggregation of Home Loads for Smart Meter Measurements." Applied Sciences 15, no. 9 (2025): 5004. https://doi.org/10.3390/app15095004.

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The use of load-monitoring systems in residential homes is fundamental in the context of smart homes and smart grids. Specifically, these systems will allow, for example, the provision of efficient energy management and/or load forecasting for residential homes. To achieve this goal, these systems can be based on the concept of a smart meter. However, a smart meter provides aggregate power consumption, which makes it extremely complex to identify individual home appliances, even using advanced algorithms. In line with this, this paper proposes to analyze the impact of power definitions on the
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9

Lindberg, K. B., S. J. Bakker, and I. Sartori. "Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts." Utilities Policy 58 (June 2019): 63–88. http://dx.doi.org/10.1016/j.jup.2019.03.004.

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10

Roth, Jonathan, Jayashree Chadalawada, Rishee K. Jain, and Clayton Miller. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification." Energies 14, no. 5 (2021): 1481. http://dx.doi.org/10.3390/en14051481.

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As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 r
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Ahajjam, Mohamed Aymane, Daniel Bonilla Licea, Mounir Ghogho, and Abdellatif Kobbane. "IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings." Sensors 20, no. 4 (2020): 1048. http://dx.doi.org/10.3390/s20041048.

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Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modula
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Yousefi, Ali, Waiching Tang, Mehrnoush Khavarian, Cheng Fang, and Shanyong Wang. "Thermal and Mechanical Properties of Cement Mortar Composite Containing Recycled Expanded Glass Aggregate and Nano Titanium Dioxide." Applied Sciences 10, no. 7 (2020): 2246. http://dx.doi.org/10.3390/app10072246.

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One of the growing concerns in the construction industry is energy consumption and energy efficiency in residential buildings. Moreover, management of non-degradable solid glass wastes is becoming a critical issue worldwide. Accordingly, incorporation of recycled expanded glass aggregates (EGA) as a substitution for natural fine aggregate in cement composites would be a sustainable solution in terms of energy consumption in the buildings and waste management. This experimental research aims to investigate the effects of EGA on fresh and hardened properties and thermal insulating performance of
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13

Olama, Mohammed, Teja Kuruganti, James Nutaro, and Jin Dong. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid." Energies 11, no. 7 (2018): 1852. http://dx.doi.org/10.3390/en11071852.

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Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a
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14

Hall, Monika, Pia Bereuter, and Achim Geissler. "Potential-estimation of thermal micro-grids in urban areas based on heat load and building clustering." Journal of Physics: Conference Series 2600, no. 2 (2023): 022016. http://dx.doi.org/10.1088/1742-6596/2600/2/022016.

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Abstract As a result of climate change, fossil heating systems must be replaced with renewable systems. The question arises whether it makes sense for each building to have its own new heating system or whether a thermal micro-grid is possible. In this paper a model is presented which allows to aggregate buildings into thermal micro-grid clusters. All gas-heated residential buildings of Basel (Switzerland) are marked via geo-data. The heat demand of each building is determined depending on the year of construction and is then converted into the heat load. Each building then is grouped into the
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15

Hosseini, Sayed Saeed, Benoit Delcroix, Nilson Henao, Kodjo Agbossou, and Sousso Kelouwani. "Towards Feasible Solutions for Load Monitoring in Quebec Residences." Sensors 23, no. 16 (2023): 7288. http://dx.doi.org/10.3390/s23167288.

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For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to
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16

Ribarov, Lubomir A., and David S. Liscinsky. "Microgrid Viability for Small-Scale Cooling, Heating, and Power." Journal of Energy Resources Technology 129, no. 1 (2006): 71–78. http://dx.doi.org/10.1115/1.2424967.

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Cooling, heating, and power (CHP) energy systems provide higher fuel efficiency than conventional systems, resulting in reduced fuel consumption, reduced emissions, and other environmental benefits. Until recently the focus of CHP system development has been primarily on medium-scale commercial applications in a limited number of market segments where clear value propositions lead to short term payback. Small-scale integrated CHP systems that show promise of achieving economic viability through significant improvements in fuel utilization have received increased attention lately. In this paper
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Kapustin, Fedor, and Vladimir A. Belyakov. "Application of Modified Peat Aggregate for Lightweight Concrete." Solid State Phenomena 309 (August 2020): 120–25. http://dx.doi.org/10.4028/www.scientific.net/ssp.309.120.

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The scientific article "Application of Modified Peat Aggregate for Lightweight Concrete" presents the results of studies of the properties of a new composite material for use in enclosing structures of residential and public buildings. Physical and mechanical characteristics of possible aggregates of local production for this type of concrete affecting its operational properties are considered. The prospects of using fly ash as an additive improving the characteristics of polystyrene concrete with the addition of modified peat have been established. The analysis was made and the optimal compos
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Syahwanti, Hezliana, and Irvhaneil Irvhaneil. "Analisis Pengaruh Penambahan Serbuk Sabut Kelapa (Cocopeat) Pada Campuran Agregat Terhadap Kuat Tekan Paving Block." Borneo Engineering : Jurnal Teknik Sipil 8, no. 2 (2024): 145–54. http://dx.doi.org/10.35334/be.v8i2.5047.

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 The use of sand in the world of construction increases every year so alternative sand companions are needed to reduce its use. One of the construction activities is making paving blocks. Paving blocks are often used for residential complex roads, sidewalks or paving yards, as a result, paving block production increases every year. This is in line with the increasing use of sand. Sand is fine grains that are physically similar to cocopeat (coconut fiber powder). Because the physical properties of cocopeat and sand are the same (Syahwanti et. al, 2021), cocopeat can be researched for use in th
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19

Nazemi, Seyyed Danial, Mohsen A. Jafari, and Esmat Zaidan. "An Incentive-Based Optimization Approach for Load Scheduling Problem in Smart Building Communities." Buildings 11, no. 6 (2021): 237. http://dx.doi.org/10.3390/buildings11060237.

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The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart
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20

Góis, João, and Lucas Pereira. "Appliance-Specific Noise-Aware Hyperparameter Tuning for Enhancing Non-Intrusive Load Monitoring Systems." Energies 18, no. 14 (2025): 3847. https://doi.org/10.3390/en18143847.

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Load disaggregation has emerged as an effective tool for enabling smarter energy management in residential and commercial buildings. By providing appliance-level energy consumption estimation from aggregate data, it supports energy efficiency initiatives, demand-side management, and user awareness. However, several challenges remain in improving the accuracy of energy disaggregation methods. For instance, the amount of noise in energy consumption datasets can heavily impact the accuracy of disaggregation algorithms, especially for low-power consumption appliances. While disaggregation performa
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Obaro, Adewale Zakariyahu, Josiah Lange Munda, and Adedayo Adedamola YUSUFF. "Modelling and Energy Management of an Off-Grid Distributed Energy System: A Typical Community Scenario in South Africa." Energies 16, no. 2 (2023): 693. http://dx.doi.org/10.3390/en16020693.

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Conventional power systems have been heavily dependent on fossil fuel to meet the increasing energy demand due to exponential population growth and diverse technological advancements. This paper presents an optimal energy model and power management of an off-grid distributed energy system (DES) capable of providing sustainable and economic power supply to electrical loads. The paper models and co-optimizes multi-energy generations as a central objective for reliable and economic power supply to electrical loads while simultaneously satisfying a set of system and operational parameters. In addi
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He, Gengsheng, Yu Huang, Ying Zhang, et al. "Hybrid Transformer–Convolutional Neural Network Approach for Non-Intrusive Load Analysis in Industrial Processes." Energies 18, no. 10 (2025): 2464. https://doi.org/10.3390/en18102464.

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With global efforts intensifying towards achieving carbon neutrality, accurately monitoring and managing energy consumption in industrial sectors has become critical. Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution for industrial energy management by decomposing aggregate power data into individual device-level information without extensive hardware requirements. However, existing NILM methods primarily tailored for residential applications struggle to capture complex inter-device correlations and production-dependent load dynamics prevalent in industrial env
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Drozdzol, Krzysztof. "Thermal and Mechanical Studies of Perlite Concrete Casing for Chimneys in Residential Buildings." Materials 14, no. 8 (2021): 2011. http://dx.doi.org/10.3390/ma14082011.

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Chimneys are structures designed to convey exhaust gases from heating devices to the outside of buildings. The materials from which they are made have a great impact on their fire safety, as well as on the safety of the whole building. As current trends in the construction industry are moving towards improving the environmental impact and fire safety, changes to building materials are constantly being introduced. This also applies to the development of chimney technology, as there is still a recognised need for new solutions when it comes to materials used in the production of chimney systems.
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Mascherbauer, Philipp, Franziska Schöniger, Lukas Kranzl, and Songmin Yu. "Impact of variable electricity price on heat pump operated buildings." Open Research Europe 2 (September 6, 2024): 135. http://dx.doi.org/10.12688/openreseurope.15268.2.

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Background Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods In this context, we take Austria as an example and aggregate the findings of i
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Mascherbauer, Philipp, Franziska Schöniger, Lukas Kranzl, and Songmin Yu. "Impact of variable electricity price on heat pump operated buildings." Open Research Europe 2 (December 7, 2022): 135. http://dx.doi.org/10.12688/openreseurope.15268.1.

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Background: Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods: In this context, we take Austria as an example and aggregate the findings of
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Yedilbayev, Bauyrzhan, Akmaral Shokanova, Zauresh Akhmetova, Gani Askarov, and Nurlan Kalganbayev. "Structural and bit-by-bit modeling of the cities." E3S Web of Conferences 159 (2020): 05001. http://dx.doi.org/10.1051/e3sconf/202015905001.

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Developed economic - ecological model of modern large city on the example of Almaty, based on the main provisions of statistical theory, theories of logistics and the similarity of the General plan of development of Kazakhstan megapolis, the strategy of transport development of Kazakhstan, programs to reduce the traffic load on the highways regulations of international and national importance, as well as on the basis of predictive decisions arising from the comprehensive consideration of the issues city transport road ecology (CTRE). It includes for the first time scientifically grounded ecolo
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Shaher, Abdullah, Saad Alqahtani, Ali Garada, and Liana Cipcigan. "Rooftop Solar Photovoltaic in Saudi Arabia to Supply Electricity Demand in Localised Urban Areas: A Study of the City of Abha." Energies 16, no. 11 (2023): 4310. http://dx.doi.org/10.3390/en16114310.

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This paper explores the potential of rooftop solar PV to meet the electricity demand in the urban areas of Abha city, Saudi Arabia (KSA), minimising imports from the grid. A localised energy system for Abha is proposed that considers two types of loads: (i) residential loads with a monthly aggregated energy consumption of 172,440 MWh and an electric demand of 239.5 MW, and (ii) commercial loads with a monthly aggregated energy consumption of 179,280 MWh and an electric demand of 249 MW. The grid currently supplies this load. This paper proposes a PV development planning tool for residential an
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Wang, Yizhen, Ningqing Zhang, and Xiong Chen. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features." Energies 14, no. 10 (2021): 2737. http://dx.doi.org/10.3390/en14102737.

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With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the re
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Hou, Tingting, Rengcun Fang, Jinrui Tang, et al. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms." Energies 14, no. 22 (2021): 7820. http://dx.doi.org/10.3390/en14227820.

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Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated l
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Lv, Wenjie, Jian Wu, Zhao Luo, et al. "Load Aggregator-Based Integrated Demand Response for Residential Smart Energy Hubs." Mathematical Problems in Engineering 2019 (April 18, 2019): 1–14. http://dx.doi.org/10.1155/2019/6925980.

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In order to attract more flexible resource to take part in integrated demand response (IDR), this can be realized by introducing load aggregator-based framework. In this paper, based on residential smart energy hubs (S.E. Hubs), a two-level IDR framework is proposed, in which S.E. Hub operators play the role of load aggregators. The framework includes day-ahead bidding and real-time scheduling. In day-ahead bidding, S.E. Hub operators have to compete dispatching amount for maximal profit; hence, noncooperative game approach is formulated to describe the competition behavior among operators. In
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Carpaneto, E., and G. Chicco. "Probabilistic characterisation of the aggregated residential load patterns." IET Generation, Transmission & Distribution 2, no. 3 (2008): 373. http://dx.doi.org/10.1049/iet-gtd:20070280.

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Yu, Heyang, Jingchen Zhang, Junchao Ma, Changyu Chen, Guangchao Geng, and Quanyuan Jiang. "Privacy-preserving demand response of aggregated residential load." Applied Energy 339 (June 2023): 121018. http://dx.doi.org/10.1016/j.apenergy.2023.121018.

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Eslami, Abolfazl, Ali Nabizadeh, and Hossein Akbarzadeh Kasani. "Geotechnical and geophysical characterisations of construction waste-infilled quarry for housing and commercial developments: Case study of Tehran, Iran." Waste Management & Research: The Journal for a Sustainable Circular Economy 40, no. 3 (2021): 349–59. http://dx.doi.org/10.1177/0734242x211052851.

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The fast population growth in the metropolitan areas of the province of Tehran has led to the scarcity of land and inevitable expansion of urban construction to non-engineered fills and construction/demolition waste disposal sites. An abandoned aggregate quarry, infilled with construction wastes over 16 years, has been recently selected for a new development project consisting of several multi-storey commercial and residential complexes (up to 7 storeys). This study was aimed at delineation of the waste materials, geophysical and field and laboratory geotechnical characterisations prior to fou
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Elias, H. Ait Aissa, and UĞURENVER Abbas. "Energy Consumption Management of Residential Appliances Based on Load Signatures Decomposition." Engineering and Technology Journal 9, no. 06 (2024): 4241–48. https://doi.org/10.5281/zenodo.12065047.

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Nowadays, energy consumption management techniques in the residential side have gained significant importance due to their considerable influence on the control of power flow in distribution networks and especially the possibility of managing a huge part of domestic electrical demand during peak-load hours. Since the customer’s data are recorded in an aggregated form, therefore, in order to apply control approaches, it is necessary to use load pattern evaluation techniques (load signature). These methods capable to decomposition effective features that help control approaches to implemen
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Seyed, Ali Hosseini, Hojjat Mehrdad, and Azarfar Azita. "An integrated home energy management system by the load aggregator in a microgrid using the internet of things infrastructure." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6796–805. https://doi.org/10.11591/ijece.v12i6.pp6796-6805.

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Smart technologies enable the significant participation of consumers in demand-side management programs. In this paper, the management of electrical energy consumption for a set of residential houses in a microgrid by a load aggregator for a 24-h planning horizon is studied. In this study, consumption management programs are implemented on controllable equipment by sending binary codes by the load aggregator via the internet of things (IoT) infrastructure to residential sockets. To increase the level of customer convenience and provide more flexibility for consumers to participate in demand re
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Hosseini, Seyed Ali, Mehrdad Hojjat, and Azita Azarfar. "An integrated home energy management system by the load aggregator in a microgrid using the internet of things infrastructure." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6796. http://dx.doi.org/10.11591/ijece.v12i6.pp6796-6805.

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<span lang="EN-US">Smart technologies enable the significant participation of consumers in demand-side management programs. In this paper, the management of electrical energy consumption for a set of residential houses in a microgrid by a load aggregator for a 24-h planning horizon is studied. In this study, consumption management programs are implemented on controllable equipment by sending binary codes by the load aggregator via the internet of things (IoT) infrastructure to residential sockets. To increase the level of customer convenience and provide more flexibility for consumers to
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Sajjad, Intisar Ali, Gianfranco Chicco, and Roberto Napoli. "Definitions of Demand Flexibility for Aggregate Residential Loads." IEEE Transactions on Smart Grid 7, no. 6 (2016): 2633–43. http://dx.doi.org/10.1109/tsg.2016.2522961.

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Huber, Mittler Patrick, Melvin Ott, Martin Friedli, Andreas Rumsch, and Andrew Paice. "Residential Power Traces for Five Houses: The iHomeLab RAPT Dataset." mdpi data 5, no. 1 (2020): 17. https://doi.org/10.5281/zenodo.3695754.

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Datasets with measurements of both solar electricity production and domestic electricity consumption separated into the major loads are interesting for research focussing on (i) local optimization of solar energy consumption and (ii) non-intrusive load monitoring. To this end, we publish the iHomeLab RAPT dataset consisting of electrical power traces from five houses in the greater Lucerne region in Switzerland spanning a period from 1.5 up to 3.5 years with a sampling frequency of five minutes. For each house, the electrical energy consumption of the aggregated household and specific applianc
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Song, Zhaofang, Jing Shi, Shujian Li, Zexu Chen, Wangwang Yang, and Zitong Zhang. "Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling." Applied Sciences 10, no. 20 (2020): 7310. http://dx.doi.org/10.3390/app10207310.

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As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechani
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40

Jia, Dexiang, Jianye Liu, Wei Lu, Chengcheng Fu, Xingde Huang, and Aiqiang Pan. "Response potential and coordinated dispatching of rural residentials’ temperature control load participating in demand-side response." Journal of Physics: Conference Series 2592, no. 1 (2023): 012063. http://dx.doi.org/10.1088/1742-6596/2592/1/012063.

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Abstract With the continuous expansion of household electricity consumption, the impact of household electricity on the power system is also increasing. TCL (Temperature Control Load) accounts for a large proportion of household electrical loads such as AC (Air Conditioning) and EWH (Electric Water Heater) and is an indispensable electrical load in households. For the temperature control load participating in DR (Demand side Response), this article first analyzes the basic physical model of TCL and uses the Monte Carlo method to aggregate multiple temperature control loads. Then, by combining
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41

Ponocko, Jelena, and Jovica V. Milanovic. "Forecasting Demand Flexibility of Aggregated Residential Load Using Smart Meter Data." IEEE Transactions on Power Systems 33, no. 5 (2018): 5446–55. http://dx.doi.org/10.1109/tpwrs.2018.2799903.

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42

Parkash, Barkha, Tek Tjing Lie, Weihua Li, and Shafiqur Rahman Tito. "End-to-End Top-Down Load Forecasting Model for Residential Consumers." Energies 17, no. 11 (2024): 2550. http://dx.doi.org/10.3390/en17112550.

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This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation is carried out with 9 (from 2013 to 2021) years of energy consumption data of 50 houses located in the United States of America. Simulation results demonstrate that the E2E model, which uses a single model for different nodes and is based on the prin
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43

Huang, Nantian, Wenting Wang, Sining Wang, Jun Wang, Guowei Cai, and Liang Zhang. "Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting." IEEE Access 8 (2020): 25198–209. http://dx.doi.org/10.1109/access.2020.2971033.

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44

Lucas, Alexandre, Luca Jansen, Nikoleta Andreadou, Evangelos Kotsakis, and Marcelo Masera. "Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector." Energies 12, no. 14 (2019): 2725. http://dx.doi.org/10.3390/en12142725.

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Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flex
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Huber, Patrick, Melvin Ott, Martin Friedli, Andreas Rumsch, and Andrew Paice. "Residential Power Traces for Five Houses: The iHomeLab RAPT Dataset." Data 5, no. 1 (2020): 17. http://dx.doi.org/10.3390/data5010017.

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Datasets with measurements of both solar electricity production and domestic electricity consumption separated into the major loads are interesting for research focussing on (i) local optimization of solar energy consumption and (ii) non-intrusive load monitoring. To this end, we publish the iHomeLab RAPT dataset consisting of electrical power traces from five houses in the greater Lucerne region in Switzerland spanning a period from 1.5 up to 3.5 years with a sampling frequency of five minutes. For each house, the electrical energy consumption of the aggregated household and specific applianc
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Carcangiu, S., A. Fanni, P. A. Pegoraro, G. Sias, and S. Sulis. "Forecasting-Aided Monitoring for the Distribution System State Estimation." Complexity 2020 (February 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/4281219.

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In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the
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Formaggio, Giovanni, Mauro Tonelli-Neto, Danieli Vilela, and Anna Lotufo. "Short-Term Forecasting of Total Aggregate Demand in Uncontrolled Residential Charging with Electric Vehicles Using Artificial Neural Networks." Inventions 10, no. 4 (2025): 54. https://doi.org/10.3390/inventions10040054.

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Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has proven to be an efficient way of solving time series problems. This study employs a multilayer perceptron network with backpropagation training and Bayesian regularisation to enhance generalisation and minimise overfitting errors. The research aggregates real consumption data from 200 households and 348 electric vehicles.
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Mumtahina, Umme, Sanath Alahakoon, Peter Wolfs, and Jiannan Liu. "Constructing Australian Residential Electricity Load Profile for Supporting Future Network Studies." Energies 17, no. 12 (2024): 2908. http://dx.doi.org/10.3390/en17122908.

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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
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Gonzalez, Reynaldo, Sara Ahmed, and Miltiadis Alamaniotis. "Implementing Very-Short-Term Forecasting of Residential Load Demand Using a Deep Neural Network Architecture." Energies 16, no. 9 (2023): 3636. http://dx.doi.org/10.3390/en16093636.

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The need for and interest in very-short-term load forecasting (VSTLF) is increasing and important for goals such as energy pricing markets. There is greater challenge in predicting load consumption for residential-load-type data, which is highly variable in nature and does not form visible patterns present in aggregated nodal-type load data. Previous works have used methods such as LSTM and CNN for VSTLF; however, the use of DNN has yet to be investigated. Furthermore, DNNs have been effectively used in STLF but have not been applied to very-short-term time frames. In this work, a deep network
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Alahyari, Arman, and Mohammad Jooshaki. "Fast energy management approach for the aggregated residential load and storage under uncertainty." Journal of Energy Storage 62 (June 2023): 106848. http://dx.doi.org/10.1016/j.est.2023.106848.

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