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Appl. Sci. 2018, 8(2), 228; https://doi.org/10.3390/app8020228

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
These authors contributed equally to this work.
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Received: 31 December 2017 / Revised: 24 January 2018 / Accepted: 28 January 2018 / Published: 2 February 2018
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Abstract

The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network’s main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility. View Full-Text
Keywords: photovoltaics; power forecasting; artificial neural networks photovoltaics; power forecasting; artificial neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Dolara, A.; Grimaccia, F.; Leva, S.; Mussetta, M.; Ogliari, E. Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning. Appl. Sci. 2018, 8, 228.

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