Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling |
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| Affiliation: | 1. College of Mathematics &Information Science, Ping Ding Shan University, Ping Ding Shan, 467000, China;2. Department of Information Management, Asia Eastern University of Science and Technology, New Taipei, 220303, Taiwan;3. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China;1. School of Mathematical Sciences, Queensland University of Technology, Australia;2. ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Australia;3. School of Computer Science, Queensland University of Technology, Australia;4. Department of Computer Science, University of Oxford, UK;5. School of Statistics and Mathematics, Guangdong University of Finance and Economics, China;1. Energy Economics Group, Management Strategy Research Office, TEPCO Research Institute, Tokyo Electric Power Company Holdings, Inc., 4-1 Egasaki-cho, Tsurumi-ku, Yokohama-shi, Kanagawa, Japan;2. Department of Statistical Sciences, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo, Japan;3. Department of Statistical Modeling, The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo, Japan;4. Graduate School of Economics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan;1. College of Mathematics &Information Science, Ping Ding Shan University, Ping Ding Shan 467000, Henan, China;1. School of Business, Qingdao University, Qingdao 200071, China;2. School of Mathematics and Statistics, Qingdao University, Qingdao 200071, China |
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| Abstract: | This paper develops a novel short-term load forecasting model that hybridizes several machine learning methods, such as support vector regression (SVR), grey catastrophe (GC (1,1)), and random forest (RF) modeling. The modeling process is based on the minimization of both SVR and risk. GC is used to process and extract catastrophe points in the long term to reduce randomness. RF is used to optimize forecasting performance by exploiting its superior optimization capability. The proposed SVR-GC-RF model has higher forecasting accuracy (MAPE values are 6.35% and 6.21%, respectively) using electric loads from Australian-Energy-Market-Operator; it can provide analytical support to forecast electricity consumption accurately. |
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| Keywords: | Support vector regression (SVR) Grey catastrophe (GC) Random forest (RF) Short term load forecasting |
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