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Abstract

Power system operations are becoming more challenging with the increasing penetration of renewable-based resources such as photovoltaic (PV) generation. In this regard, obtaining accurate solar power output forecasts allows a deepening penetration of renewable-based resources in a secure and reliable way. In this paper, we propose a probabilistic framework to predict short-term PV output taking into account the uncertainty of weather data as well as the variability of PV output over time. To this end, we use datasets comprising of meteorological weather data such as temperature, irradiance, zenith, and azimuth and solar power output. We cluster these data in categories and train a Matern 5/2 Gaussian Process Regression model for each cluster. ´More specifically, we cluster the data into one to eight different partitions by making use of the k-means algorithm. In order to identify the optimal number of clusters we use the Elbow and Gap methods. We compare the results obtained for the different number of clusters with the (i) 5-fold cross-validation; and (ii) holding out 30 representative days as test data. The results showed that the optimal number of clusters is four, since in comparison to higher number of clusters the increase in the forecast error was marginal.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments:	School of Science & Technology > Computer Science School of Science & Technology > Engineering

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