Cooling Load Prediction Method Using Reinforcement Learning Based on Attention Mechanism
CHEN Xiyao
ZHANG Ying
ZHAO Lifan
HE Kun
CHEN Jianping
Abstract：Short-term building cooling load forecasting is an important basis for many building energy management tasks.In view of the fact that the traditional prediction model takes low-correlation features as input,which will lead to the reduction of pre-diction accuracy of the model,an attention mechanism-based deep reinforcement learning(Deep Deterministic Policy Gradient,AM-DDPG)short-term building cooling load prediction method is proposed.The method first normalizes the data.Second,the pre-diction problem is modeled as a Markov decision process.The state is the current weather data and the historical cooling load.The action is the predicted value of the building cooling load in the next hour.The reward is the difference between the actual value and the predicted value of the building cooling load in the next hour.Again,the attention mechanism is added to the actor network of deep reinforcement learning.Finally,the prediction model is obtained by learning from the data and updating the network parame-ters.The experimental results show that under the mean absolute error evaluation index,AM-DDPG is 22.12%,10.99%and 21.40%lower than BP,LSTM and DDPG respectively.Under the evaluation index of root mean square error,AM-DDPG is lower than BP,LSTM and DDPG are reduced by 10.43%,4.58%and 7.09%respectively.
Keywords：deep reinforcement learningattention mechanismcooling load prediction
Publication Date：2025-06-20
Online Publishing Date：2025-09-23(First online date of this platform, not the publication date of the document)
Pages：7( 1591-1597 )