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Article

Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

1
Department of Computer Science, Universidad Carlos III de Madrid, 28911 Madrid, Spain
2
Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
*
Authors to whom correspondence should be addressed.
Energies 2019, 12(1), 100; https://doi.org/10.3390/en12010100
Received: 29 November 2018 / Revised: 19 December 2018 / Accepted: 25 December 2018 / Published: 29 December 2018
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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. View Full-Text
Keywords: smart persistence; photovoltaic forecasting; random forests smart persistence; photovoltaic forecasting; random forests
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MDPI and ACS Style

Huertas Tato, J.; Centeno Brito, M. Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production. Energies 2019, 12, 100. https://doi.org/10.3390/en12010100

AMA Style

Huertas Tato J, Centeno Brito M. Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production. Energies. 2019; 12(1):100. https://doi.org/10.3390/en12010100

Chicago/Turabian Style

Huertas Tato, Javier; Centeno Brito, Miguel. 2019. "Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production" Energies 12, no. 1: 100. https://doi.org/10.3390/en12010100

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