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Federated learning-based short-term building energy consumption prediction method for solving the data silos problem

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Building Simulation 2022年第6期15卷 1145-1159页

作者： Junyang Li Chaobo Zhang Yang Zhao Weikang Qiu Qi Chen Xuejun Zhang Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhouChina Department of Energy Engineering Zhejiang UniversityHangzhouChina Zhejiang Energy Group Co. Ltd.HangzhouChina

Transfer learning is an effective method to predict the energy consumption of information-poor buildings by learning transferable knowledge from operational data of information-rich buildings.However,it is not recommended to directly use the operational data without protection due to the risk of leaking occupants’privacy.To address this problem,this study proposes a federated learning-based method to learn transferable knowledge from building operational data without privacy leaking.It trains a transferable federated model based on the operational data from the buildings similar to the target building with limited data.An advanced secure aggregation algorithm is adopted in the training process to ensure that no one can infer private information from the training data.The federated model obtained is evaluated by comparing it with the standalone model without federated learning based on 13 similar office buildings from the Building data Genome Project.The results show that the federated model outperforms the standalone model concerning the prediction accuracy and training time.On average,the federated model achieves a 25.4%decrease in CV-RMSE when the target building has limited operational data.Even if the target building has no operational data,the federated model still achieves acceptable accuracy(CV-RMSE is 22.2%).Meanwhile,the training time of the federated model is 90%less than that of the standalone model.The research insights can help develop federated learning-based methods for solving the data silos problem in building energy management.The methodology and analysis procedures are reproducible and all codes and data sets are available on Github.

关键词： building energy consumption prediction federated learning transfer learning data privacy protection data silos

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security.data is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our reality.In the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied level.The paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： data silos Machine learning Federated learning Privacy protection Learning framework

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