Trends and gaps in photovoltaic power forecasting with machine learning

A. Alcañiz*, D. Grzebyk, H. Ziar, O. Isabella

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
95 Downloads (Pure)

Abstract

The share of solar energy in the electricity mix increases year after year. Knowing the production of photovoltaic (PV) power at each instant of time is crucial for its integration into the grid. However, due to meteorological phenomena, PV power output can be uncertain and continuously varying, which complicates yield prediction. In recent years, machine learning (ML) techniques have entered the world of PV power forecasting to help increase the accuracy of predictions. Researchers have seen great potential in this approach, creating a vast literature on the topic. This paper intends to identify the most popular approaches and the gaps in this discipline. To do so, a representative part of the literature consisting of 100 publications is classified based on different aspects such as ML family, location of PV systems, number of systems considered, features, etc. Via this classification, the main trends and gaps can be highlighted while offering advice to researchers interested in the topic.
Original languageEnglish
Pages (from-to)447-471
Number of pages25
JournalEnergy Reports
Volume9
DOIs
Publication statusPublished - 2022

Funding

The research leading to these results has received funding from the Horizon 2020 Programme , under Grant Agreement 952957, Trust-PV project.

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