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Energies 2016, 9(12), 1017; doi:10.3390/en9121017

Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation

Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, 54224 Abu Dhabi, UAE
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Author to whom correspondence should be addressed.
Academic Editor: José C. Riquelme
Received: 18 July 2016 / Revised: 24 November 2016 / Accepted: 28 November 2016 / Published: 1 December 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
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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. View Full-Text
Keywords: solar power; probabilistic forecasting; regression; machine learning; ensemble models solar power; probabilistic forecasting; regression; machine learning; ensemble models
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ahmed Mohammed, A.; Aung, Z. Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation. Energies 2016, 9, 1017.

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