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Combining forecasts of day-ahead solar power

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  • Dewangan, Chaman Lal
  • Singh, S.N.
  • Chakrabarti, S.

Abstract

Solar power forecasting is important for the reliable and economic operation of power systems with high penetration of solar energy. The solar power forecasts for the day-ahead time horizon are more erroneous than the hour-ahead time horizon. Numerical weather prediction (NWP) variables such as irradiance, cloud cover, precipitation etc. are used as input to day-ahead forecasting models. The uncertainty in NWP varies with weather conditions. Different forecasting algorithms based on a single method are available in the literature. Combination of individual forecasting algorithms increases the accuracy of the forecasts. However, the combined-forecast has yet not been analysed much in the area of day-ahead solar power forecasting. This paper thus explores different combined-forecast methods such as mean, median, linear regression and non-linear regressions using supervised machine learning algorithms. The number of models required for day-ahead solar power forecasts is studied. One for all hour (same) or separate models for each hour of the day are possible. The effects of retraining frequency on the performance of the forecasting models, which is important for the computational burden of the system, are also studied. Forecasting algorithms are applied to three solar plants in Australia.

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  • Dewangan, Chaman Lal & Singh, S.N. & Chakrabarti, S., 2020. "Combining forecasts of day-ahead solar power," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220308501
    DOI: 10.1016/j.energy.2020.117743
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    1. Graham Elliott & Allan Timmermann, 2016. "Forecasting in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 81-110, October.
    2. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    3. Alonso-Montesinos, J. & Monterreal, R. & Fernández-Reche, J. & Ballestrín, J. & Carra, E. & Polo, J. & Barbero, J. & Batlles, F.J. & López, G. & Enrique, R. & Martínez-Durbán, M. & Marzo, A., 2019. "Intra-hour energy potential forecasting in a central solar power plant receiver combining Meteosat images and atmospheric extinction," Energy, Elsevier, vol. 188(C).
    4. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    5. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    6. Gupta, Akshita & Kumar, Arun & Khatod, Dheeraj Kumar, 2019. "Optimized scheduling of hydropower with increase in solar and wind installations," Energy, Elsevier, vol. 183(C), pages 716-732.
    7. Huang, Jing & Perry, Matthew, 2016. "A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1081-1086.
    8. Afrasiabi, Mousa & Mohammadi, Mohammad & Rastegar, Mohammad & Kargarian, Amin, 2019. "Multi-agent microgrid energy management based on deep learning forecaster," Energy, Elsevier, vol. 186(C).
    9. Juban, Romain & Ohlsson, Henrik & Maasoumy, Mehdi & Poirier, Louis & Kolter, J. Zico, 2016. "A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1094-1102.
    10. Graabak, I. & Korpås, M. & Jaehnert, S. & Belsnes, M., 2019. "Balancing future variable wind and solar power production in Central-West Europe with Norwegian hydropower," Energy, Elsevier, vol. 168(C), pages 870-882.
    11. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
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    Cited by:

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    2. Brester, Christina & Kallio-Myers, Viivi & Lindfors, Anders V. & Kolehmainen, Mikko & Niska, Harri, 2023. "Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations," Renewable Energy, Elsevier, vol. 207(C), pages 266-274.
    3. Liu, Guanjun & Qin, Hui & Shen, Qin & Lyv, Hao & Qu, Yuhua & Fu, Jialong & Liu, Yongqi & Zhou, Jianzhong, 2021. "Probabilistic spatiotemporal solar irradiation forecasting using deep ensembles convolutional shared weight long short-term memory network," Applied Energy, Elsevier, vol. 300(C).
    4. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    5. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    6. Tukymbekov, Didar & Saymbetov, Ahmet & Nurgaliyev, Madiyar & Kuttybay, Nurzhigit & Dosymbetova, Gulbakhar & Svanbayev, Yeldos, 2021. "Intelligent autonomous street lighting system based on weather forecast using LSTM," Energy, Elsevier, vol. 231(C).
    7. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    8. Visser, Lennard & AlSkaif, Tarek & van Sark, Wilfried, 2022. "Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution," Renewable Energy, Elsevier, vol. 183(C), pages 267-282.
    9. Huang, Yanmei & Hasan, Najmul & Deng, Changrui & Bao, Yukun, 2022. "Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting," Energy, Elsevier, vol. 239(PC).
    10. Yang, Dazhi & Yang, Guoming & Liu, Bai, 2023. "Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction," Renewable Energy, Elsevier, vol. 215(C).
    11. Ladislav Zjavka, 2021. "Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation," Energies, MDPI, vol. 14(22), pages 1-14, November.

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