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• W2558903719 startingPage "1017" @default.
• W2558903719 abstract "Probabilistic forecasting accounts for the uncertainty in prediction that arises from inaccurate input data due to measurement errors, as well as the inherent inaccuracy of a prediction model. Because of the variable nature of renewable power generation depending on weather conditions, probabilistic forecasting is well suited to it. For a grid-tied solar farm, it is increasingly important to forecast the solar power generation several hours ahead. In this study, we propose three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h ahead solar power forecasts. We have shown that while all of the individual machine learning models are more accurate than the traditional benchmark models, like autoregressive integrated moving average (ARIMA), the ensemble models offer even more accurate results than any individual machine learning model alone does. Furthermore, it is observed that running separate models on the data belonging to the same hour of the day vastly improves the accuracy of the results. Getting more accurate forecasts will help the stakeholders come up with better decisions in resource planning and control when large-scale solar farms are integrated into the power grid." @default.
• W2558903719 created "2016-12-08" @default.
• W2558903719 creator A5005863795 @default.
• W2558903719 creator A5050977652 @default.
• W2558903719 date "2016-12-01" @default.
• W2558903719 modified "2023-10-05" @default.
• W2558903719 title "Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation" @default.
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• W2558903719 doi "https://doi.org/10.3390/en9121017" @default.
• W2558903719 hasPublicationYear "2016" @default.
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