基于分类DQN的建筑能耗预测

本文提出一种可用于建筑能耗预测的基于KNN分类器的DQN算法——K-DQN. 其在利用马尔科夫决策过程对建筑能耗进行建模时, 针对大规模动作空间问题, 将原始动作空间缩减进而提高算法的预测精度及收敛速率. 首先, K-DQN将原始动作空间平均划分为多个子动作空间, 并将每个子动作空间对应的状态分为一类, 以此构建KNN分类器. 其次, 利用KNN分类器, 将不同类别相同次序动作进行统一表示, 以实现动作空间的缩减. 最后, K-DQN将状态类别概率与原始状态相结合, 在构建新状态的同时, 帮助确定缩减动作空间内每一动作的具体含义, 从而确保算法的收敛性. 实验结果表明, 文章提出的K-DQN算法可以获得优于DDPG、DQN算法的能耗预测精度, 且降低了网络训练时间.

DQN Based on Classifier for Building Energy Consumption Prediction

This study proposes a deep Q-network (DQN) algorithm based on the K-nearest neighbor (KNN) algorithm (K-DQN) for the energy consumption prediction of buildings. When using the Markov decision process to model the energy consumption of buildings, the K-DQN algorithm shrinks the original action space to improve the prediction accuracy and convergence rate considering large-scale action space problems. Firstly, the original action space is evenly divided into multiple sub-action spaces, and the corresponding state of each sub-action space is regarded as a class to construct the KNN algorithm. Secondly, actions of the same sequence in different classes are denoted by the KNN algorithm to shrink the original action space. Finally, state class probabilities and original states are combined by K-DQN to construct new states and help determine the meaning of each action in the shrunken action space, which can ensure the convergence of the K-DQN algorithm. The experimental results indicate that the proposed K-DQN algorithm can achieve higher prediction accuracy than deep deterministic policy gradient (DDPG) and DQN algorithms and take less network training time.