Nikolaou, N; Batzelis, E; Brown, G; (2017) Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. In: Woon, WL and Aung, Z and Kramer, O and Madnick, S, (eds.) DARE 2017: Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. (pp. pp. 13-25). Springer: Cham, Switzerland.

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Abstract

The energy yield estimation of a photovoltaic (PV) system operating under partially shaded conditions is a challenging task and a very active area of research. In this paper, we attack this problem with the aid of machine learning techniques. Using data simulated by the equivalent circuit of a PV string operating under partial shading, we train and evaluate three different gradient boosted regression tree models to predict the global maximum power point (MPP). Our results show that all three approaches improve upon the state-of-the-art closed-form estimates, in terms of both average and worst-case performance. Moreover, we show that even a small number of training examples is sufficient to achieve improved global MPP estimation. The methods proposed are fast to train and deploy and allow for further improvements in performance should more computational resources be available.

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