Abstract
Probabilistic forecasts account for the uncertainty in the prediction helping the decision makers take optimal decisions. With the emergence of renewable technologies and the uncertainties involved with the power generated through them, probabilistic forecasts can come to the rescue. Wind power is a mature technology and is in place for decades now, various probabilistic forecasting techniques are used here. On the other hand solar power is an emerging technology and as the technology matures there will be a need for forecasting the power generated days ahead. In this study, we utilize some of the probabilistic forecasting techniques in the field of solar power forecasting. An ensemble approach is used with different machine learning algorithms and different initial settings assuming normal distribution for the forecasts. It is observed that having multiple models with different initial settings gives exceedingly better results when compared to individual models. Getting accurate forecasts will be of great help where the large scale solar farms are integrated into the power grid.
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References
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Bacher, P., Madsen, H., Nielsen, H.A.: Online short-term solar power forecasting. Sol. Energy 83(10), 1772–1783 (2009)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Breiman, L., Friedman, J., Stone, C., Olshen, R.A.: Classification and regression trees. Taylor & Francis (1984)
Davidson, D.J., Andrews, J.: Not all about consumption. Science 339(6125), 1286–1287 (2013)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)
GEFCOM: Global energy forecasting competition 2014. http://www.drhongtao.com/gefcom (2014)
Gneiting, T., Katzfuss, M.: Probabilistic forecasting. Ann. Rev. Stat. Appl. 1, 125–151 (2014)
Goldemberg, J., Johansson, T.B., Anderson, D.: World energy assessment: overview: 2004 Update. United Nations Development Programme, Bureau for Development Policy (2004)
Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)
Hong, T.: Energy forecasting: past, present, and future. Foresight: Int. J. Appl. Forecast. Winter 2014, 43–48 (2014)
Hossain, M.R., Oo, A.M.T., Shawkat Ali, A.B.M.: Hybrid prediction method for solar power using different computational intelligence algorithms. Smart Grid renew. Energy 4(1), 76–87 (2013)
Huang, Y., Lu, J., Liu, C., Xu, X., Wang, W., Zhou, X.: Comparative study of power forecasting methods for PV stations. In: Proceedings of the 2010 IEEE International Conference on Power System Technology (POWERCON), pp. 1–6. IEEE (2010)
International Energy Agency: International energy outlook 2013. http://www.eia.gov/forecasts/archive/ieo13 (2013)
International Energy Agency: Technology roadmap: solar photovoltaic energy—2014 edition. www.iea.org/publications/freepublications/publication/technology-roadmap-solar-photovoltaic-energy--2014-edition.html (2014)
Iversen, E.B., Morales, J.M., Møller, J.K., Madsen, H.: Probabilistic forecasts of solar irradiance using stochastic differential equations. Environmetrics 25(3), 152–164 (2014)
Koenker, R.: Quantile Regression. Cambridge University Press, New York (2005)
Letendre, S.E.: Grab the low-hanging fruit: use solar forecasting before storage to stabilize the grid. http://www.renewableenergyworld.com/rea/news/article/2014/10/grab-the-low-hanging-fruit-of-grid-integration-with-solar-forecasting (2014)
Marquez, R., Coimbra, C.F.M.: Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Sol. Energy 85(5), 746–756 (2011)
Perera, K.S., Aung, Z., Woon, W.L.: Machine learning techniques for supporting renewable energy generation and integration: a survey. In: Data Analytics for Renewable Energy Integration—Second ECML PKDD Workshop, DARE 2014, Lecture Notes in Computer Science, vol. 8817, pp. 81–96 (2014)
Runyon, J.: Transparency and better forecasting tools needed for the solar industry. http://www.renewableenergyworld.com/rea/news/article/2012/12/transparency-and-better-forecasting-tools-needed-for-the-solar-industry (2015)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc.: Ser. (Methodol.) 58(1), 267–288 (1996)
Wikipedia: Solar power forecasting. http://en.wikipedia.org/wiki/Solar_power_forecasting (2015)
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Mohammed, A.A., Yaqub, W., Aung, Z. (2015). Probabilistic Forecasting of Solar Power: An Ensemble Learning Approach. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_38
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DOI: https://doi.org/10.1007/978-3-319-19857-6_38
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