Advanced Methods for Photovoltaic Output Power Forecasting: A Review

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 ap...

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Vydáno v:Applied sciences Ročník 10; číslo 2; s. 487
Hlavní autoři: Mellit, Adel, Massi Pavan, Alessandro, Ogliari, Emanuele, Leva, Sonia, Lughi, Vanni
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2020
Témata:
Accuracy
Algorithms
Artificial intelligence
artificial intelligence techniques
Computers
Deep learning
Logic programming
Machine learning
Neural networks
photovoltaic plant
power forecasting
Support vector machines
Time series
Weather forecasting
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app10020487