Energy optimization leveraging artificially intelligent algorithms has been proven effective. However, when buildings are commissioned, there is no historical data that could be used to train these algorithms. On-line Reinforcement Learning (RL) algorithms have shown significant promise, but their deployment carries a significant risk, because as the RL agent initially explores its action space it could cause significant discomfort to the building residents. In this paper we present ReLBOT - a new technique that uses transfer learning in conjunction with deep RL to transfer knowledge from an existing, optimized and instrumented building, to the newly commissioning smart building, to reduce the adverse impact of the reinforcement learning agent's warm-up period. We demonstrate improvements of up to 6.2 times in the duration, and up to 132 times in prediction variance, for the reinforcement learning agent's warm-up period.

翻译：利用人工智能算法进行能源优化已被证明是有效的。然而，当建筑投入使用之时，并无可用于训练这些算法的历史数据。在线强化学习算法显示出显著潜力，但其部署存在重大风险，因为在强化学习代理最初探索其动作空间时，可能导致建筑居民严重不适。在本文中，我们提出ReLBOT——一种新技术，该技术将迁移学习与深度强化学习相结合，将知识从现有已优化且配备了仪表的建筑迁移至新投入使用的智能建筑，以减少强化学习代理预热阶段的不利影响。我们展示了强化学习代理预热阶段持续时间最多提升6.2倍，预测方差最多改进132倍的成果。