Cited by Smart energy management: Process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems

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

Optimal loading distribution of chillers based on an improved beluga whale optimization for reducing energy consumption DOI

Ze Li, Jiayi Gao,

Junfei Guo

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 307, P. 113942 - 113942

Published: Feb. 3, 2024

Language: Английский

Citations

Enhancing Air Conditioning System Efficiency Through Load Prediction and Deep Reinforcement Learning: A Case Study of Ground Source Heat Pumps DOI

Zhitao Wang,

Yubin Qiu,

Shiyu Zhou

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(1), P. 199 - 199

Published: Jan. 5, 2025

This study proposes a control method that integrates deep reinforcement learning with load forecasting, to enhance the energy efficiency of ground source heat pump systems. Eight machine models are first developed predict future cooling loads, and optimal one is then incorporated into learning. Through interaction environment, strategy identified using Q-network optimize supply water temperature from source, allowing for savings. The obtained results show XGBoost model significantly outperforms other in terms prediction accuracy, reaching coefficient determination 0.982, mean absolute percentage error 6.621%, variation root square 10.612%. Moreover, savings achieved through forecasting-based greater than those traditional constant methods by 10%. Additionally, without shortening interval, improved 0.38% compared do not use predictive information. approach requires only continuous between agent which makes it an effective alternative scenarios where sensor equipment data present. It provides smart adaptive optimization solution heating, ventilation, air conditioning systems buildings.

Language: Английский

Citations

Performance evaluation and optimization of the cascade refrigeration system based on the digital twin model DOI

Yanpeng Li,

Yiwei Feng,

Chuang Wang

et al.

Applied Thermal Engineering, Journal Year: 2024, Volume and Issue: 248, P. 123160 - 123160

Published: April 10, 2024

Language: Английский

Citations

Comparative Analysis of Hybrid Heating and Cooling Systems DOI

Faisal D. Al-Ghamdi,

Moncef Krarti

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112480 - 112480

Published: March 1, 2025

Language: Английский

Citations

Evaluating seasonal chiller performance using operational data DOI

Si Wu,

Pu Yang,

Guanghao Chen

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124377 - 124377

Published: Sept. 6, 2024

Language: Английский

Citations

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function DOI

Youyi Chen, Kyoung Hyun Kwak,

Jaewoong Kim

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 58898 - 58914

Published: Jan. 1, 2024

The thermal management system (TMS) in electric vehicles (EVs), including climate control and battery regulation, consumes more energy than any other auxiliary components. Therefore, optimizing TMS is crucial for enhancing EV driving range. However, the complexity of TMS, described by a differential algebraic system, poses challenges real-time optimal control. This study proposes model predictive (MPC)-based solutions integrated operation EVs. An problem formulated using economic nonlinear MPC (NMPC), its performance evaluated. To reduce computational load, an approximated value function (VF) introduced based on NMPC results. A linear-time-varying (LTV-MPC) with VF proposed implementation quadratic programming, through simulations it compared baseline controller rule-based (RB) controller. Results reveal that LTV-MPC performs similarly to while offering slightly compromised cooling performance. It also significantly reduces time factor 10 ⁴ owing short prediction horizon enabled VF. Furthermore, when RB controller, achieves savings range 22.3% 29.8%.

Language: Английский

Citations

Accelerating chiller sequencing using dynamic programming DOI

S. Li, Siqi Li, Zhe Wang

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 325, P. 115037 - 115037

Published: Nov. 9, 2024

Language: Английский

Citations

Smart energy management: Process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems DOI

Long Wu,

Xunyuan Yin,

Lei Pan

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 380, P. 124965 - 124965

Published: Nov. 29, 2024

Language: Английский

Citations