Real-time prediction intervals for intra-hour DNI forecasts |
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| Affiliation: | 1. School of Energy and Environment, Southeast University, Nanjing 210096, China;2. Ministry of Education of Key Laboratory of Energy Thermal Conversion and Control, Southeast University, Nanjing 210096, China;3. Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology, Southeast University, Nanjing 210096, China;1. Department of Food Engineering, URI – Campus de Erechim, Av. Sete de Setembro, 1621, Erechim, RS 99700-000, Brazil;2. Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, CEP 88800-000 Florianópolis, SC, Brazil;3. LASEFI/DEA/FEA (School of Food Engineering)/UNICAMP (University of Campinas), Rua Monteiro Lobato, 80, 13083-862 Campinas, SP, Brazil;4. Federal University of Fronteira Sul, Erechim, Av. Dom João Hoffmann, Erechim 99700-000, Brazil;5. Department of Chemical Engineering, Federal University of Santa Maria, Av. Roraima, 1000, Santa Maria, RS 97105-900, Brazil;1. School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, 4001, Australia;2. Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, Jiangsu Province, 212013, China;1. School of Economics and Management, North China Electric Power University, Beijing 102206, China;2. School of Natural and Built Environments, University of South Australia, Adelaide 5001, Australia |
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| Abstract: | We develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20 min. This hybrid model, which integrates sky imaging techniques, support vector machine and artificial neural network sub-models, is developed using one year of co-located, high-quality irradiance and sky image recording in Folsom, California. We validate the proposed model using six-month of measured irradiance and sky image data, and apply it to construct operational PI forecasts in real-time at the same observatory. In the real-time scenario, the hybrid model significantly outperforms the reference persistence model and provides high performance PIs regardless of forecast horizon and weather condition. |
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| Keywords: | Solar forecasting Prediction intervals Sky imaging Support vector machines Artificial neural networks |
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