Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms

Taşkın Tez; Erhan Akyol

doi:10.31015/2025.1.24

EN

Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms

Abstract

Predicting solar power generation is used to ensure that solar power plants operate with optimum efficiency, meet the demands of the energy grid and stabilize energy prices. This study aims to predict the medium-term electricity generation of photovoltaic panels with machine learning algorithms. Boosting Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Neural Network Regression, Random Forest Regression, Regularized Linear Regression, and Support Vector Machine Regression algorithms were evaluated. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-Squared (R²) were calculated. It was found that the Random Forest algorithm has the best prediction metrics. A hypothesis was formulated to evaluate the difference between the actual energy generation of the photovoltaic panels and the predicted energy by the Random Forest algorithm. The hypothesis was evaluated by the Mann-Whitney U hypothesis test and the p-value was calculated as greater than 0.05. It was concluded that there is no significant difference between the predicted energy by the Random Forest (RF) algorithm and the actual energy generated by photovoltaic panels. Based on the results of this study, we recommend using the Random Forest algorithm for medium-term energy generation prediction for photovoltaic solar panels.

Keywords

Photovoltaic, Energy, Prediction, Machine Learning

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Details

Primary Language

English

Subjects

Agricultural Energy Systems

Journal Section

Research Article

Authors

Taşkın Tez
0000-0002-9837-3213
Türkiye

Erhan Akyol ^*
0000-0001-8121-8121
Türkiye

Publication Date

March 17, 2025

Submission Date

February 3, 2025

Acceptance Date

March 13, 2025

Published in Issue

Year 2025 Volume: 9 Number: 1

DOI

https://doi.org/10.31015/2025.1.24

IZ

https://izlik.org/JA83JA89UE

APA

Tez, T., & Akyol, E. (2025). Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms. International Journal of Agriculture Environment and Food Sciences, 9(1), 221-232. https://doi.org/10.31015/2025.1.24

AMA

1.Tez T, Akyol E. Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms. int. j. agric. environ. food sci. 2025;9(1):221-232. doi:10.31015/2025.1.24

Chicago

Tez, Taşkın, and Erhan Akyol. 2025. “Estimation of Energy Production of Solar Panels Installed in Agricultural Areas With Machine Learning Algorithms”. International Journal of Agriculture Environment and Food Sciences 9 (1): 221-32. https://doi.org/10.31015/2025.1.24.

EndNote

Tez T, Akyol E (March 1, 2025) Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms. International Journal of Agriculture Environment and Food Sciences 9 1 221–232.

IEEE

[1]T. Tez and E. Akyol, “Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms”, int. j. agric. environ. food sci., vol. 9, no. 1, pp. 221–232, Mar. 2025, doi: 10.31015/2025.1.24.

ISNAD

Tez, Taşkın - Akyol, Erhan. “Estimation of Energy Production of Solar Panels Installed in Agricultural Areas With Machine Learning Algorithms”. International Journal of Agriculture Environment and Food Sciences 9/1 (March 1, 2025): 221-232. https://doi.org/10.31015/2025.1.24.

JAMA

1.Tez T, Akyol E. Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms. int. j. agric. environ. food sci. 2025;9:221–232.

MLA

Tez, Taşkın, and Erhan Akyol. “Estimation of Energy Production of Solar Panels Installed in Agricultural Areas With Machine Learning Algorithms”. International Journal of Agriculture Environment and Food Sciences, vol. 9, no. 1, Mar. 2025, pp. 221-32, doi:10.31015/2025.1.24.

Vancouver

1.Taşkın Tez, Erhan Akyol. Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms. int. j. agric. environ. food sci. 2025 Mar. 1;9(1):221-32. doi:10.31015/2025.1.24

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Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms

Abstract

Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Abstracting & Indexing Services

© International Journal of Agriculture, Environment and Food Sciences