Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105959 - 105959
Published: Nov. 1, 2024
Language: Английский
Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105959 - 105959
Published: Nov. 1, 2024
Language: Английский
Communications Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1)
Published: Feb. 22, 2025
Language: Английский
Citations
0Building Simulation, Journal Year: 2025, Volume and Issue: unknown
Published: April 29, 2025
Language: Английский
Citations
0Applied Energy, Journal Year: 2025, Volume and Issue: unknown, P. 125387 - 125387
Published: Jan. 1, 2025
Language: Английский
Citations
0Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112880 - 112880
Published: March 1, 2025
Language: Английский
Citations
0Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 124, P. 204 - 213
Published: April 2, 2025
Language: Английский
Citations
0Buildings, Journal Year: 2024, Volume and Issue: 14(9), P. 2645 - 2645
Published: Aug. 26, 2024
The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables storage presents significant challenges for traditional home management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as the most efficient approach tackling these problems because its robust nonlinear fitting capacity capability to operate without a predefined model. This paper DRL control method intended lower expenses elevate usage optimizing actions battery heat pump HEMS. We propose four algorithms thoroughly assess their performance. In pursuit this objective, we also devise new reward function multi-objective optimization an interactive environment grounded expert experience. results demonstrate that TD3 algorithm excels cost savings PV self-consumption. Compared baseline model, model achieved 13.79% reduction operating costs 5.07% increase Additionally, explored impact feed-in tariff (FiT) on TD3’s performance, revealing resilience even when FiT decreases. comparison provides insights into selection specific applications, promoting development DRL-driven solutions.
Language: Английский
Citations
2Renewable Energy, Journal Year: 2024, Volume and Issue: 237, P. 121619 - 121619
Published: Oct. 13, 2024
Language: Английский
Citations
2Energy, Journal Year: 2024, Volume and Issue: 307, P. 132607 - 132607
Published: July 31, 2024
Language: Английский
Citations
1Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105959 - 105959
Published: Nov. 1, 2024
Language: Английский
Citations
0