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Advanced Methods for Photovoltaic Output Power Forecasting: A Review

1
RELab, University of Jijel, Jijel 18000, Algeria
2
Dipartimento di Ingegneria e Architettura, University of Trieste, 34127 Trieste, Italy
3
Department of Energy, Politecnico di Milano, 20156 Milano, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 487; https://doi.org/10.3390/app10020487
Received: 13 November 2019 / Revised: 30 December 2019 / Accepted: 30 December 2019 / Published: 9 January 2020
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems - Volume II)
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic. View Full-Text
Keywords: photovoltaic plant; power forecasting; artificial intelligence techniques; machine learning; deep learning photovoltaic plant; power forecasting; artificial intelligence techniques; machine learning; deep learning
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MDPI and ACS Style

Mellit, A.; Massi Pavan, A.; Ogliari, E.; Leva, S.; Lughi, V. Advanced Methods for Photovoltaic Output Power Forecasting: A Review. Appl. Sci. 2020, 10, 487.

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