concept

Home Energy Management System

Also known as: HEMS, HEM, Home Energy Management Controller

Facts (31)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 28 facts

measurementBarolli et al. (2020) reported that using Grey Wolf Optimizer (GWO) and Bacterial Foraging Optimization (BFO) techniques in a Home Energy Management System (HEMS) resulted in 45% and 55% energy reductions, respectively.

referenceAhmed MS, Mohamed A, Khatib T, Shareef H, Homod RZ, and Abd Ali J published the study 'Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm' in the journal Energy and Buildings in 2017.

claimMahmood et al. (2016) recommended a Home Energy Management Controller (HEMC) model to control appliance scheduling, which lowers Peak-to-Average Ratio (PAR) and electricity costs but may lead to energy waste and disregard for environmental concerns.

referenceIqbal Z, Javaid N, Iqbal S, Aslam S, Khan ZA, Abdul W, Almogren A, and Alamri A published 'A domestic microgrid with optimized home energy management system' in 2018 in the journal Energies, volume 11, issue 4, page 1002.

referenceKhan ZA, Zafar A, Javaid S, Aslam S, Rahim MH, and Javaid N (2019) designed a home energy management system for smart grids based on hybrid meta-heuristic optimization.

referenceKhan ZA, Zafar A, Javaid S, Aslam S, Rahim MH, and Javaid N (2019) developed a hybrid meta-heuristic optimization-based home energy management system for smart grids, published in the Journal of Ambient Intelligence and Humanized Computing.

claimMahmood et al. (2016) recommended a Home Energy Management Controller (HEMC) model to control appliance scheduling, which lowers user comfort, Peak-to-Average Ratio (PAR), and electricity costs, though it may result in energy waste and disregard environmental concerns.

referenceZhao Z, Lee WC, Shin Y, and Song K-B published 'An optimal power scheduling method for demand response in home energy management system' in IEEE Transactions on Smart Grid, volume 4, issue 3, pages 1391–1400, in 2013.

referenceHuang Y, Wang L, Guo W, Kang Q, and Wu Q (2016) published 'Chance constrained optimization in a home energy management system,' which explores the application of chance-constrained optimization techniques to home energy management systems.

referenceYang H-T, Yang C-T, Tsai C-C, Chen G-J, and Chen S-Y (2015) developed an improved Particle Swarm Optimization (PSO) based home energy management system integrated with demand response for smart grids, presented at the 2015 IEEE Congress on Evolutionary Computation.

referenceHuang Y, Wang L, Guo W, Kang Q, and Wu Q (2016) implemented chance-constrained optimization within a home energy management system.

claimAmbreen K, Khalid R, Maroof R, Khan HN, Asif S, and Iftikhar H implemented critical peak pricing in home energy management systems using biography-based optimization and genetic algorithms in 2017.

referenceAlthaher S, Mancarella P, and Mutale J published the study 'Automated demand response from home energy management system under dynamic pricing and power and comfort constraints' in the IEEE Transactions on Smart Grid in 2015.

referenceElmouatamid et al. (2020) evaluated the performance of a Home Energy Management System (HEMS) using three meta-heuristic optimization techniques: Harmony Search (HS), Bacterial Foraging Optimization (BFO), and EDE algorithms.

claimAhmad A, Khan A, Javaid N, Hussain HM, Abdul W, Almogren A, Alamri A, and Azim Niaz I proposed an optimized home energy management system that integrates renewable energy and storage resources in a 2017 study.

claimWang et al. (2012) developed an ideal dispatching model for a smart Home Energy Management System (HEMS) using Mixed Integer Nonlinear Programming (MINLP) to reduce electricity costs and total energy consumption.

referenceHuang Y, Tian H, and Wang L (2015) published 'Demand response for home energy management system' in International Journal of Electrical Power & Energy Systems, volume 73, pages 448–455, which discusses demand response strategies within home energy management systems.

referenceIn the context of energy management systems, the abbreviation HEMS stands for Home energy management system.

claimReinforcement learning involves determining how agents should perform actions in an environment to maximize cumulative rewards, and Q-learning is commonly used at the Home Energy Management System (HEMS) level to optimize appliance scheduling using cost and user comfort as reward functions (O’Neill et al. 2010; Wen et al. 2015).

referenceShafie-Khah and Siano (2017) introduced a stochastic home energy management system that considers satisfaction cost and response fatigue, published in IEEE Transactions on Industrial Informatics, 14(2):629–638.

referenceRajendhar P and Jeyaraj BE published a review in 2019 titled 'Application of DR and co-simulation approach for renewable integrated HEMS: a review' in IET Generation, Transmission & Distribution.

referenceHussain HM, Javaid N, Iqbal S, Hasan QU, Aurangzeb K, and Alhussein M published 'An efficient demand side management system with a new optimized home energy management controller in smart grid' in 2018 in the journal Energies, volume 11, issue 1, page 190.

referenceIqbal et al. (2018) proposed a domestic microgrid design featuring an optimized home energy management system, published in Energies.

claimO’Neill et al. (2010) and Wen et al. (2015) state that Q-learning is commonly used at the Home Energy Management System (HEMS) level to optimize appliance scheduling by using cost and user comfort as reward functions.

claimElmouatamid et al. (2020) evaluated the performance of a Home Energy Management System (HEMS) using three meta-heuristic optimization techniques: Harmony Search (HS), Bacterial Foraging Optimization (BFO), and EDE algorithms.

claimAhmed MS, Mohamed A, Khatib T, Shareef H, Homod RZ, and Abd Ali J developed a real-time optimal schedule controller for home energy management systems using a new binary backtracking search algorithm in 2017.

referenceHussain et al. (2018) developed an efficient demand-side management system utilizing a new optimized home energy management controller for smart grids, published in Energies.

referenceZhao Z, Lee WC, Shin Y, and Song K-B published 'An optimal power scheduling method for demand response in home energy management system' in IEEE Transactions on Smart Grid in 2013.

Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 1 fact

referenceThe research paper 'An optimized home energy management system with integrated renewable energy and storage resources' was published in Energies 10(4), 549 in 2017.

A Comprehensive Review on Residential Demand Side Management ideas.repec.org MDPI 1 fact

referenceJia et al. published 'Data compression approach for the home energy management system' in the journal Applied Energy in 2019.

Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 1 fact

referenceThe GreenTech Nexus system includes smart meters for real-time energy tracking, user interfaces for interactive management, scheduler modules for energy allocation, Home Energy Management (HEM) systems, smart appliances, photovoltaic (PV) panels, Battery Energy Storage Systems (BESS), and electric vehicles (EVs) that function as both loads and energy reservoirs.