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Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO
Institution:1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China;2. Network and communication Center, Lanzhou University, Lanzhou, China;1. School of Economics and Management, North China Electric Power University, Beijing 102206, China;2. Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China;3. Business School, Hunan University, Changsha 410082, China;4. Center for Resource and Environmental Management, Hunan University, Changsha 410082, China;5. China Electric Power Research Institute, Beijing 100192, China;6. Shanghai Electric Power Company, Shanghai 200122, China;1. Department of Operations Research, Wroc?aw University of Technology, Wroc?aw, Poland;2. CERGE-EI, Prague, Czech Republic;1. Department of Computer & Information Technology Engineering, Amirkabir University of Technology, Tehran, Iran;2. Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran;3. School of Science, Technology, Engineering and Mathematics (STEM), University of Washington, Bothell, USA;4. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran;5. Department of Decision Science and Knowledge Engineering, University of Economics, Tehran, Iran
Abstract:We use a unique set of prices from the German EPEX market and take a closer look at the fine structure of intraday markets forelectricity, with their continuous trading for individual load periods up to 30 min before delivery. We apply the least absolute shrinkage and selection operator (LASSO) in order to gain statistically sound insights on variable selection and provide recommendations for very short-term electricity price forecasting.
Keywords:Intraday electricity market  Variable selection  Price forecasting  LASSO  ARX model  Diebold-Mariano test  Trading strategy
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