Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression
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- Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
- Safi, S. & Zeroual, A. & Hassani, M., 2002. "Prediction of global daily solar radiation using higher order statistics," Renewable Energy, Elsevier, vol. 27(4), pages 647-666.
- Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
- Dougherty, Christopher, 2011. "Introduction to Econometrics," OUP Catalogue, Oxford University Press, edition 4, number 9780199567089.
- Che-Jung Chang & Der-Chiang Li & Wen-Li Dai & Chien-Chih Chen, 2013. "Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-6, July.
- Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
- Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko & Hesham S. Rabayah & Raed M. Abendeh & Rami Alawneh, 2023. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations," Energies, MDPI, vol. 16(13), pages 1-24, June.
- Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
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PV power generation; forecast; multiple regression; energy transition;All these keywords.
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