一种面向建筑节能的强化学习自适应控制方法

Building energy efficiency oriented reinforcement learning adaptive control method

针对建筑节能领域中传统控制方法对于建筑物相关设备控制存在收敛速度慢、不稳定等问题,结合强化学习中经典的Q学习方法,提出一种强化学习自适应控制方法——RLAC。该方法通过对建筑物内能耗交换机制进行建模,结合Q学习方法,求解最优值函数,进一步得出最优控制策略,确保在不降低建筑物人体舒适度的情况下,达到建筑节能的目的。将所提出的RLAC与On/Off以及Fuzzy-PD方法用于模拟建筑物能耗问题进行对比实验,实验结果表明,RLAC具有较快的收敛速度以及较好的收敛精度。

With respect to the problem of slow convergence and instability for the traditional methods, in the field of building energy efficiency, this paper proposes a new reinforcement learning adaptive control method, RLAC by combining Q-learning. The proposed method models the exchange mechanism of the building energy consumption, and tries to find the better control policy by solving the optimal value function. Furthermore, RLAC can decrease the energy consumption without losing the performance of good comfort of the building occupants. Compared with the On/Off and Fuzzy-PD, the proposed RLAC has a better convergence performance in speed and accuracy.