Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning

Maria Krechowicz, Adam Krechowicz, Lech Lichołai, Artur Pawelec, Jerzy Zbigniew Piotrowski and Anna Stępień
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Maria Krechowicz: Faculty of Management and Computer Modelling, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland
Adam Krechowicz: Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland
Lech Lichołai: Faculty of Civil Engineering, Environmental Engineering and Architecture, Rzeszow University of Technology, ul. Poznańska 2, 35-959 Rzeszow, Poland
Artur Pawelec: Faculty of Management and Computer Modelling, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland
Jerzy Zbigniew Piotrowski: Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland
Anna Stępień: Faculty of Civil Engineering and Architecture, Kielce University of Technology, al. 1000-lecia P.P. 7, 25-314 Kielce, Poland

Energies, 2022, vol. 15, issue 11, 1-21

Abstract: Problems with inaccurate prediction of electricity generation from photovoltaic (PV) farms cause severe operational, technical, and financial risks, which seriously affect both their owners and grid operators. Proper prediction results are required for optimal planning the spinning reserve as well as managing inertia and frequency response in the case of contingency events. In this work, the impact of a number of meteorological parameters on PV electricity generation in Poland was analyzed using the Pearson coefficient. Furthermore, seven machine learning models using Lasso Regression, K–Nearest Neighbours Regression, Support Vector Regression, AdaBoosted Regression Tree, Gradient Boosted Regression Tree, Random Forest Regression, and Artificial Neural Network were developed to predict electricity generation from a 0.7 MW solar PV power plant in Poland. The models were evaluated using determination coefficient ( R 2 ), the mean absolute error ( M A E ), and root mean square error ( R M S E ). It was found out that horizontal global irradiation and water saturation deficit have a strong proportional relationship with the electricity generation from PV systems. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed PV farm. Random Forest Regression was the most reliable and accurate model, as it received the highest R 2 (0.94) and the lowest M A E (15.12 kWh) and R M S E (34.59 kWh).

Keywords: photovoltaic systems; PV farm; machine learning; risk reduction (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 (3)

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