Optimal Scheduling of Residential Heating, Ventilation and Air Conditioning Based on Deep Reinforcement Learning
Abstrak
Residential heating, ventilation and air condition‐ ing (HVAC) provides important demand response resources for the new power system with high proportion of renewable ener‐ gy. Residential HAVC scheduling strategies that adapt to realtime electricity price signals formulated by demand response program and ambient temperature can significantly reduce elec‐ tricity costs while ensuring occupants’ comfort. However, since the pricing process and weather conditions are affected by many factors, conventional model-based method is difficult to meet the scheduling requirements in complex environments. To solve this problem, we propose an adaptive optimal scheduling strategy for residential HVAC based on deep reinforcement learning (DRL) method. The scheduling problem can be regard‐ ed as a Markov decision process (MDP). The proposed method can adaptively learn the state transition probability to make economical decision under the tolerance violations. Specifically, the residential thermal parameters obtained by the leastsquares parameter estimation (LSPE) can provide a basis for the state transition probability of MDP. Daily simulations are verified under the electricity prices and temperature data sets, and numerous experimental results demonstrate the effective‐ ness of the proposed method.
Penulis (6)
M. Xia
Fangjian Chen
Qifang Chen
Siwei Liu
Yuguang Song
T. Wang
Akses Cepat
- Tahun Terbit
- 2023
- Bahasa
- en
- Total Sitasi
- 17×
- Sumber Database
- Semantic Scholar
- DOI
- 10.35833/mpce.2022.000249
- Akses
- Open Access ✓