Publication: Using smart persistence and random forests to predict photovoltaic energy production
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2019-01-01
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MDPI
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Abstract
Solar energy forecasting is an active research problem and a key issue to increase the competitiveness of solar power plants in the energy market. However, using meteorological, production, or irradiance data from the past is not enough to produce accurate forecasts. This article aims to integrate a prediction algorithm (Smart Persistence), irradiance, and past production data, using a state-of-the-art machine learning technique (Random Forests). Three years of data from six solar PV modules at Faro (Portugal) are analyzed. A set of features that combines past data, predictions, averages, and variances is proposed for training and validation. The experimental results show that using Smart Persistence as a Machine Learning input greatly improves the accuracy of short-term forecasts, achieving an NRMSE of 0.25 on the best panels at short horizons and 0.33 on a 6 h horizon.
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This article belongs to the Special Issue Solar and Wind Energy Forecasting.
Keywords
Smart persistence, Photovoltaic forecasting, Random forests, Solar irradiance, Forecasting methods, Model
Bibliographic citation
Huertas-Tato, J., & Brito, M. (2019). Using smart persistence and random forests to predict photovoltaic energy production. Energies, 12(1), 100.