Cited by A HEN-PPO strategy for home energy management system with reduce EV anxieties

A comprehensive review of predictive control strategies in heating, ventilation, and air-conditioning (HVAC): Model-free VS model DOI

Xin Xin, Zhihao Zhang, Yong Zhou

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 94, P. 110013 - 110013

Published: June 21, 2024

Language: Английский

Citations

Multi-agent deep reinforcement learning based HVAC control for multi-zone buildings considering zone-energy-allocation optimization DOI

Wenping Xue, Ning Jia, Ming Zhao

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: 329, P. 115241 - 115241

Published: Jan. 2, 2025

Language: Английский

Citations

Flexible coupling and grid-responsive scheduling assessments of distributed energy resources within existing zero energy houses DOI

Xiaoyi Zhang, Fu Xiao, Yanxue Li

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 87, P. 109047 - 109047

Published: March 16, 2024

Language: Английский

Citations

Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building DOI

Navid Morovat, Andreas Athienitis, José A. Candanedo

et al.

Energy, Journal Year: 2024, Volume and Issue: 296, P. 131126 - 131126

Published: March 29, 2024

This paper presents a heuristic model predictive control (MPC) methodology to activate energy flexibility in fully electric school buildings cold climates reduce electricity demand during peak periods of the grid. To streamline implementation MPC, proposed approach employs grey-box archetypes, clustering weather conditions identify typical scenarios and limited number possible setpoint profiles. A data-driven is used create archetype models for different thermal zones building; this enables rapid development requires much less calibration data than black-box models. third-order resistance-capacitance network with convective heating fourth-order radiant floor are developed calibrated using measured from an all-electric building Québec, Canada. The clustered into several categories, representing (6 clusters two ambient temperature ranges three solar radiation ranges). MPC strategy uses predefined optimal profiles each cluster prediction one day ahead shift load on-peak off-peak hours. For scenario, runs simulation forecast quantify response grid requirements. framework was implemented as case study. Ten classrooms investigated, six four reference reactive system default zone Results indicate that can provide between 47% 95% (load shifting relative reference) hours up 44% cost reduction while satisfying acceptable constraints. By implementing 32 W/m2 area 65 be achieved event. generalized replicated other buildings.

Language: Английский

Citations

AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings DOI

Dalia Mohammed Talat Ebrahim Ali,

Violeta Motuzienė, Rasa Džiugaitė-Tumėnienė

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(17), P. 4277 - 4277

Published: Aug. 27, 2024

Despite the tightening of energy performance standards for buildings in various countries and increased use efficient renewable technologies, it is clear that sector needs to change more rapidly meet Net Zero Emissions (NZE) scenario by 2050. One problems have been analyzed intensively recent years operation much than they were designed to. This problem, known as gap, found many often attributed poor management building systems. The application Artificial Intelligence (AI) Building Energy Management Systems (BEMS) has untapped potential address this problem lead sustainable buildings. paper reviews different AI-based models proposed applications with intention reduce consumption. It compares evaluated reviewed papers presenting accuracy error rates model identifies where greatest savings could be achieved, what extent. review showed offices (up 37%) when employ AI HVAC control optimization. In residential educational buildings, lower intelligence existing BEMS results smaller 23% 21%, respectively).

Language: Английский

Citations

Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications DOI

Mingzhe Liu, Mingyue Guo, Yangyang Fu

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 372, P. 123753 - 123753

Published: June 25, 2024

Language: Английский

Citations

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings DOI

Zekun Shi,

Ruifan Zheng,

Jun Zhao

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 320, P. 118995 - 118995

Published: Sept. 4, 2024

Language: Английский

Citations

Reinforcement Learning for Control and Optimization of Real Buildings: Identifying and Addressing Implementation Hurdles DOI

Lotta Kannari, Nina Wessberg,

Sara Hirvonen

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112283 - 112283

Published: March 1, 2025

Language: Английский

Citations

Individual room air-conditioning control in high-insulation residential building during winter: A deep reinforcement learning-based control model for reducing energy consumption DOI

Luning Sun, Zehuan Hu, Masayuki MAE

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: unknown, P. 114799 - 114799

Published: Sept. 1, 2024

Language: Английский

Citations

Prospects and Challenges of Reinforcement Learning- Based HVAC Control DOI

Ajifowowe Iyanu,

Hojong Chang,

C Lee

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 111080 - 111080

Published: Oct. 1, 2024

Language: Английский

Citations