Multiple households energy consumption forecasting using consistent modeling with privacy preservation |
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| Affiliation: | 1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, China;2. College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore;3. College of Science and Engineering, James Cook University, Cairns, QLD 4870, Australia;1. ISAE-SUPMECA, Quartz Laboratory, Saint-Ouen, France;2. Roberval Laboratory, University of Technology of Compiègne, Compiègne, France;3. Laboratory of Mechanics of Sousse, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia;1. School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing, PR China;2. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, PR China;3. State Key Laboratory of Virtual Reality Technology and System, Beijing, PR China;1. College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Shandong, Qingdao 266590, China;2. College of Intelligent Equipment, Shandong University of Science and Technology, Shandong, Taian 271019, China;1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan;2. Science & Technology Policy Research and Information Center, National Applied Research Laboratories, Taipei 106, Taiwan |
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| Abstract: | Traditional data-driven energy consumption forecasting models, including machine learning and deep learning methods, showed outstanding performance in terms of forecasting accuracy and efficiency. The superior performances are based on enough training data samples. Moreover, the derived forecasting model is only applicable to the training dataset and usually is applied to specific household. In real-world smart city development, a centralized forecasting model is required to model and forecasting energy consumption patterns for multiple households, whereas the traditional data-driven forecasting approaches may become invalid. A consistent model is demanded in this scenario modeling multiple households’ energy consumption patterns. Additionally, privacy issues are also highly concerned in such scenarios. Accurate energy consumption forecasting with privacy preservations becomes a key point for the state-of-art research. In this study, we adopt an innovative privacy-preserving structure that combines deep learning and federated learning. Under the premise of guaranteeing forecasting accuracy and privacy preservation, this structure can achieve the forecasting of various household energy consumption with a consistent model that simultaneously forecast multiple household energy consumption data by transmission control protocol. |
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| Keywords: | Deep learning Federated learning Bidirectional Long Short-Term Memory Household energy consumption forecasting |
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