concept

home energy management systems

Also known as: HEMS, HEMSs, smart home energy management, home energy management, smart home energy management systems

Facts (40)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 29 facts

referenceJavaid N, Mohsin SM, Iqbal A, Yasmeen A, and Ali I presented 'A hybrid bat-crow search algorithm based home energy management in smart grid' at the 2018 Conference on Complex, Intelligent, and Software Intensive Systems.

referenceBeaudin and Zareipour published a review titled 'Home energy management systems: a review of modelling and complexity' in 2015.

referenceTariq M, Khalid A, Ahmad I, Khan M, Zaheer B, and Javaid N presented research on load scheduling in home energy management systems using meta-heuristic techniques and critical peak pricing tariffs at the International Conference on P2P, Parallel, Grid, Cloud and Internet Computing in 2017.

referenceShareef et al. (2018) reviewed home energy management systems considering demand responses, smart technologies, and intelligent controllers, published in IEEE Access, 6:24498–24509.

referenceBeaudin M and Zareipour H published 'Home energy management systems: a review of modelling and complexity' in 2015.

referenceJavaid et al. (2018) presented a hybrid bat-crow search algorithm for home energy management in smart grids at the Conference on Complex, Intelligent, and Software Intensive Systems.

referenceKhan AA, Razzaq S, Khan A, and Khursheed F (2015) provided an overview of Home Energy Management Systems (HEMSs) and their role in enabling demand response within the electricity market, published in Renewable and Sustainable Energy Reviews.

referenceHuang Y, Tian H, and Wang L (2015) developed a demand response model for home energy management systems.

claimWang et al. (2012) developed an ideal dispatching model for smart Home Energy Management Systems (HEMS) with distributed energy resources and smart home appliances using the mixed integer nonlinear programming (MINLP) methodology, which decreases both electricity costs and total energy usage.

referenceAnvari-Moghaddam A, Monsef H, and Rahimi-Kian A published 'Optimal smart home energy management considering energy saving and a comfortable lifestyle' in IEEE Transactions on Smart Grid in 2014.

referenceWang et al. (2012) developed an optimal dispatching model for smart home energy management systems, presented at the IEEE PES Innovative Smart Grid Technologies conference.

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

referenceAbideen ZU, Jamshaid F, Zahra A, Rehman AU, Razzaq S, and Javaid N published 'Meta-heuristic and nature inspired approaches for home energy management' in the proceedings of the international conference on network-based information systems in 2017.

claimAlthaher S, Mancarella P, and Mutale J developed an automated demand response system for home energy management that operates under dynamic pricing and power/comfort constraints in 2015.

referenceKhan AA, Razzaq S, Khan A, and Khursheed F (2015) provided an overview of Home Energy Management Systems (HEMSs) and their role in enabling demand response within the electricity market.

referencePilloni et al. (2016) proposed a Quality of Experience (QoE)-driven approach for smart home energy management that incorporates renewable energy sources.

referenceLu X, Zhou K, Chan FT, and Yang S published research on the optimal scheduling of household appliances for smart home energy management, specifically considering demand response, in the journal Natural Hazards in 2017.

referenceAmbreen K, Khalid R, Maroof R, Khan HN, Asif S, and Iftikhar H presented the paper 'Implementing critical peak pricing in home energy management using biography based optimization and genetic algorithm in smart grid' at the International Conference on Broadband and Wireless Computing, Communication and Applications in 2017.

referencePilloni V et al. (2016) proposed a Quality of Experience (QoE)-driven approach for smart home energy management that includes renewable energy sources, published in IEEE Transactions on Smart Grid.

referenceAbideen ZU, Jamshaid F, Zahra A, Rehman AU, Razzaq S, and Javaid N published 'Meta-heuristic and nature inspired approaches for home energy management' in the proceedings of the International Conference on Network-Based Information Systems (2017).

referenceLeitao J, Gil P, Ribeiro B, and Cardoso A (2020) conducted a survey on home energy management, published in IEEE Access 8:5699–5722.

referenceAl Essa MJM published the study 'Home energy management of thermostatically controlled loads and photovoltaic-battery systems' in the journal Energy in 2019.

claimJavaid et al. developed the bat-crow search algorithm (BCSA) by combining the meta-heuristic bat algorithm (BA) and the crow search algorithm (CSA) for Home Energy Management Systems (HEMS) using critical peak pricing (CPP).

referenceYao et al. (2015) suggested an autonomous energy scheduling strategy to solve the problem of voltage escalation in Home Energy Management Systems (HEMS).

referenceYao et al. (2015) proposed an autonomous energy scheduling strategy designed to resolve voltage escalation problems in Home Energy Management Systems (HEMS).

referenceLeitao J, Gil P, Ribeiro B, and Cardoso A published 'A survey on home energy management' in IEEE Access in 2020.

claimJavaid et al. developed the bat-crow search algorithm (BCSA) by combining a meta-heuristic bat algorithm (BA) and a crow search algorithm (CSA) for Home Energy Management Systems (HEMS) using the critical peak pricing (CPP) system.

referenceAnvari-Moghaddam et al. published a study titled 'Optimal smart home energy management considering energy saving and a comfortable lifestyle' in the IEEE Transactions on Smart Grid in 2014.

referenceTariq M, Khalid A, Ahmad I, Khan M, Zaheer B, and Javaid N researched load scheduling in home energy management systems using meta-heuristic techniques and critical peak pricing tariffs, presented at the 2017 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 11 facts

referenceKanakadhurga and Prabaharan (2024) researched smart home energy management using demand response, incorporating uncertainty analysis of electric vehicles in the presence of renewable energy sources.

referenceKanakadhurga and Prabaharan examined smart home energy management using demand response while incorporating uncertainty analysis of electric vehicles in the presence of renewable energy sources to enhance decision-making frameworks for dynamic load control.

claimThe Beluga Whale Optimization Algorithm (BWOA), inspired by the feeding behavior of whales, outperforms the Salp Swarm Algorithm (SSA) in Home Energy Management Systems by utilizing advanced optimization techniques that enable faster convergence, dynamic adaptation to environmental conditions, and superior performance in minimizing electricity costs and maintaining grid stability.

claimThe Beluga Whale Optimization Algorithm (BWOA) is an optimization method inspired by the feeding behavior of whales that outperforms the Salp Swarm Algorithm (SSA) in Home Energy Management Systems (HEMS) by utilizing advanced techniques for faster convergence and dynamic adjustment to environmental conditions.

claimIntelligent users utilize technologies such as Advanced Metering Infrastructure (AMI), communicating thermostats, and home energy management systems to respond to real-time pricing signals and time-of-use rates, allowing them to shift consumption to off-peak periods.

claimThe Beluga Whale Optimization Algorithm (BWOA) is an optimization method inspired by the feeding behavior of whales that consistently outperforms the Salp Swarm Algorithm (SSA) in optimizing Home Energy Management Systems (HEMS).

claimKanakadhurga and Prabaharan examined smart home energy management using demand response and uncertainty analysis of electric vehicles in the presence of renewable energy sources to improve decision-making frameworks for dynamic load control.

claimIntelligent users utilize technologies such as Advanced Metering Infrastructure (AMI), communicating thermostats, and home energy management systems to respond dynamically to real-time pricing signals and time-of-use rates, allowing them to shift consumption to off-peak periods.

claimThe Salp Swarm Algorithm (SSA), inspired by the swarming behavior of salps, is an optimization method that demonstrates strengths in global exploration and convergence but is less effective than the Beluga Whale Optimization Algorithm (BWOA) in load shifting and electricity cost reduction for Home Energy Management Systems.

referenceKanakadhurga and Prabaharan published research in Applied Energy in 2024 on smart home energy management using demand response, incorporating uncertainty analysis of electric vehicles in the presence of renewable energy sources.

referenceKanakadhurga and Prabaharan examined smart home energy management using demand response and uncertainty analysis of electric vehicles in the presence of renewable energy sources to improve decision-making frameworks for dynamic load control.