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Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources by Adam Krechowicz; Maria Krechowicz; Katarzyna Poczeta is a Engineering article available to read on EtoBox.

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Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the

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Author

Adam Krechowicz; Maria Krechowicz; Katarzyna Poczeta

Publisher

MDPI AG

Published

2022

Language

EN

Field

Engineering (Physical Sciences)