Closed-Loop Building Energy Control via Deep Forecasting, Reinforcement Learning and Evolutionary Multi-Objective Optimization in Hot-Summer/Cold-Winter Zon
Abstract
This study proposes a closed-loop building energy control framework for green buildings in hot-summer/cold-winter zones, integrating a three-layer LSTM with attention for short-term load forecasting, a PPO-based reinforcement learning agent for adaptive demand response, and NSGA-II for multi-objective optimization of energy efficiency, comfort, and equipment lifespan. A dataset of 12 office buildings (14 M records over two years) supports training and validation. The forecasting module is evaluated using MAE and RMSE, achieving 6.8% MAE. Comparative experiments with PID, MPC, and single-algorithm baselines show that the proposed method achieves 91.3% energy utilization, an average response delay of 1.9 s, and a comfort compliance rate of 92.4%. Results from both simulation and field deployment confirm the framework’s adaptability and stability under price fluctuations, meteorological disturbances, and multi-building collaboration.
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DOI: https://doi.org/10.31449/inf.v49i14.11233
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