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Energies 2016, 9(1), 55; doi:10.3390/en9010055

A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting

Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Texas Sustainable Energy Research Institute, San Antonio, TX 78249, USA
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Guido Carpinelli
Received: 15 October 2015 / Revised: 28 December 2015 / Accepted: 11 January 2016 / Published: 19 January 2016
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We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system. View Full-Text
Keywords: artificial neural network (ANN); support vector regression (SVR); photovoltaic (PV) forecasting artificial neural network (ANN); support vector regression (SVR); photovoltaic (PV) forecasting

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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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Li, Z.; Rahman, S.M.; Vega, R.; Dong, B. A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting. Energies 2016, 9, 55.

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