Deep learning has been advocated as the predominant modeling method in the next-generation green building energy systems for energy prediction, predictive maintenance and control optimization. However, in response to external changes, the limited adaptability to new contents and catastrophic forgetting of previously learnt knowledge result in diminished accuracy and robustness, significantly blocking its practical application. To this end, a novel lifelong learning method with deep generative replay was proposed for dynamic and adaptive modeling to conserve energy and mitigate emissions in building energy systems. The presented lifelong learning method was characterized by the alternate training of the task solver and the replay generator in sequential energy task learning to alleviate the catastrophic forgetting. The replay generator provided the past data for the task solver to retain previous energy knowledge while learn new information, which was a conditional generative model instead of explicitly storing data to save resources and protect privacy. In order to validate its technical feasibility, a field experiment was conducted in a specially constructed net zero energy building for the case study on solar power generation prediction. The overall accuracy of proposed method was 53.4% higher than the standard method through fine-tuning and reached 0.89, which closely approaches the theoretical upper bound of 0.91 obtained by the joint training. Moreover, the proposed method effectively retained previously learnt knowledge in sequential energy task learning, evidenced by an average forgetting rate lower than 0.10. Furthermore, extensive comparative experiments have demonstrated the superiority of the proposed method over other machine learning models based on retraining or incremental training. Our study is expected to develop more flexible and robust deep learning models for improving energy efficiency and promoting the carbon neutrality in building energy systems.
