High-resolution probabilistic load forecasting: A learning ensemble approach |
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| Institution: | 1. Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China;2. School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, Guangdong, China;1. College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China;2. School of Automation, Southeast University, Nanjing 210096, China;3. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyonsan 38541, South Korea;1. College of Artificial Intelligence, Nankai University, Tianjin 300650, China;2. Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, Nankai University, Tianjin 300650, China;1. Engineering Research Center of Internet of Things Technology and Applications (Ministry of Education), Jiangnan University, Wuxi, Jiangsu 214122, China;2. Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea;1. Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China;2. Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 200092, China;1. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China;2. Center for Control Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China;1. Department of Mathematics, Guizhou University, Guiyang, Guizhou 550025, PR China;2. Gui’an Supercomputing Center, Kechuang Industrial Development Company Limited, Gui’an New Area, Guiyang, Guizhou 550025, PR China;3. CHP-LCOCS, School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, China |
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| Abstract: | High-resolution probabilistic load forecasting can comprehensively characterize both the uncertainties and the dynamic trends of the future load. Such information is key to the reliable operation of the future power grid with a high penetration of renewables. To this end, various high-resolution probabilistic load forecasting models have been proposed in recent decades. Compared with a single model, it is widely acknowledged that combining different models can further enhance the prediction performance, which is called the model ensemble. However, existing model ensemble approaches for load forecasting are linear combination-based, like mean value ensemble, weighted average ensemble, and quantile regression, and linear combinations may not fully utilize the advantages of different models, seriously limiting the performance of the model ensemble. We propose a learning ensemble approach that adopts the machine learning model to directly learn the optimal nonlinear combination from data. We theoretically demonstrate that the proposed learning ensemble approach can outperform conventional ensemble approaches. Based on the proposed learning ensemble model, we also introduce a Shapley value-based method to evaluate the contributions of each model to the model ensemble. The numerical studies on field load data verify the remarkable performance of our proposed approach. |
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