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

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. INDEX TERMS Heating load, forecasting, energy management, building, cyber-secure federated learning, deep learning. ABBREVIATIONS HVAC Heating, ventilation and air conditioning ANNs Artificial neural networks ELM Extreme learning machine MILP Mixed-integer linear programming DNN Deep neural network LSTM Long short-term memory CSFDL Cyber-secure federated deep learning SVR Support vector regrssion GRNN General regression neural network Bi-LSTM Bidirectional long short-term memory The associate editor coordinating the review of this manuscript and approving it for publication was Hao Wang. FDL Federated deep learning FedAvg Federated average CNN Convolutional neural network R 2 Correlation coefficient RMSE Root mean square error MAE Mean absolute error MAPE Mean absolute percentage error RNNs Recurrent neural networks I. INTRODUCTION Presently, energy is considered an essential resource for most aspects of life, and plays an important role in human lifestyle. On the other hand, sustainable economic development around