Day ahead forecasting for solar and wind electricity production using machine learning techniques
Date Issued
2023-07
Author(s)
Angelovski, Andrej
Dedinec, Aleksandra
Abstract
This paper explores the forecasting of renewable
energy power output, specifically wind power in Bogdanci and
solar farms in the Republic of N. Macedonia, with the end
goal of achieving a day ahead forecast. Accurate forecasting of
renewable energy is critical for reliable and efficient energy gen eration, making this study important for the energy industry and
policymakers. The study uses historical data from MEPSO for
the last 4 years (since 2020) and employs various statistical and
machine learning techniques, including autocorrelation function
(ACF), partial autocorrelation function (PACF), periodogram,
linear regression, decision tree regressor, random forest regressor,
support vector machine, XGBRegressor, Lasso, and Ridge, to
predict, or rather forecast power output. Results indicate that
accurate forecasting can be achieved using these methods, with
potential implications for the adoption of renewable energy
sources. The models were evaluated using mean squared error,
mean absolute error, and r2 score.
energy power output, specifically wind power in Bogdanci and
solar farms in the Republic of N. Macedonia, with the end
goal of achieving a day ahead forecast. Accurate forecasting of
renewable energy is critical for reliable and efficient energy gen eration, making this study important for the energy industry and
policymakers. The study uses historical data from MEPSO for
the last 4 years (since 2020) and employs various statistical and
machine learning techniques, including autocorrelation function
(ACF), partial autocorrelation function (PACF), periodogram,
linear regression, decision tree regressor, random forest regressor,
support vector machine, XGBRegressor, Lasso, and Ridge, to
predict, or rather forecast power output. Results indicate that
accurate forecasting can be achieved using these methods, with
potential implications for the adoption of renewable energy
sources. The models were evaluated using mean squared error,
mean absolute error, and r2 score.
Subjects
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