Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection  

作　　者：You Lu Linqian Cui YunzheWang Jiacheng Sun Lanhui Liu 

机构地区：[1]School of Electronic and Information Engineering,Suzhou University of Science and Technology,Suzhou,215009,China [2]Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency,Suzhou University of Science and Technology,Suzhou,215009,China [3]Chongqing Industrial Big Data Innovation Center Co.,Ltd.,Chongqing,400707,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第10期717-732,共16页工程与科学中的计算机建模（英文）

基　　金：supported by National Key R&D Program of China(No.2020YFC2006602);National Natural Science Foundation of China(Nos.62172324,62072324,61876217,6187612);University Natural Science Foundation of Jiangsu Province(No.21KJA520005);Primary Research and Development Plan of Jiangsu Province(No.BE2020026);Natural Science Foundation of Jiangsu Province(No.BK20190942).

摘　　要：Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

关 键 词：Energy consumption forecasting federated learning data privacy protection Q-LEARNING 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]