Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Optimal Scheduling of Residential Heating, Ventilation and Air Conditioning Based on Deep Reinforcement Learning

1. School of Electrical Engineering, Beijing Jiaotong University, Beijing, China
2. State Grid State Power Economic Research Institute, Beijing, China

Fund Project:

This work was supported in part by the Fundamental Research Funds for the Central Universities (No. 2018JBZ004) and the National Natural Science Foundation of China (No. 52007004).

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials

    Residential heating, ventilation and air conditioning (HVAC) provides important demand response resources for the new power system with high proportion of renewable energy. Residential HAVC scheduling strategies that adapt to real-time electricity price signals formulated by demand response program and ambient temperature can significantly reduce electricity 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 regarded 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 least-squares 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 effectiveness of the proposed method.

    Cited by
Get Citation
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
  • Received:April 28,2022
  • Revised:July 05,2022
  • Adopted:
  • Online: September 20,2023
  • Published: