Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources

Adam Krechowicz (), Maria Krechowicz and Katarzyna Poczeta
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Adam Krechowicz: Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, 25-314 Kielce, Poland
Maria Krechowicz: Faculty of Management and Computer Modelling, Kielce University of Technology, 25-314 Kielce, Poland
Katarzyna Poczeta: Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, 25-314 Kielce, Poland

Energies, 2022, vol. 15, issue 23, 1-41

Abstract: 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 analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper.

Keywords: machine learning; deep learning; extreme learning machine; renewable energy sources; electricity production forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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