A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

• Published in: IEEE access Vol. 10; pp. 5037 - 5050
• Main Authors: Moradzadeh, Arash, Moayyed, Hamed, Mohammadi-Ivatloo, Behnam, Aguiar, A. Pedro, Anvari-Moghaddam, Amjad
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: building; Buildings; Clients; Comparative studies; cyber-secure federated learning; Cybersecurity; Deep learning; Demand; Economic forecasting; energy management; Forecasting; Heating load; Heating systems; Load forecasting; Load modeling; Predictive models; Privacy
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3139529

Recently, with the establishment of new thermal regulation, the energy efficiency of buildings has increased significantly, and various deep learning-based methods have been presented to accurately forecast the heating load demand of buildings. However, all of these methods are executed on a dataset with specific distribution and do not have the property of global forecasting, and have no guarantee of data privacy against cyber-attacks. This paper presents a novel approach to heating load demand forecasting based on Cyber-Secure Federated Deep Learning (CSFDL). The suggested CSFDL provides a global super-model for forecasting heating load demand of different local clients without knowing their location and, most importantly, without revealing their privacy. In this study, a CSFDL global server is trained and tested considering the heating load demand of 10 different clients in their building environment. The presented results, including a comparative study, prove the viability and accuracy of the proposed procedure.