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

Zhaoxuan Li, Mahbobur Rahman Sm, Rolando Vega and Bing Dong
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Zhaoxuan Li: Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Mahbobur Rahman Sm: Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Rolando Vega: Texas Sustainable Energy Research Institute, San Antonio, TX 78249, USA
Bing Dong: Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA

Energies, 2016, vol. 9, issue 1, 1-12

Abstract: 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.

Keywords: artificial neural network (ANN); support vector regression (SVR); photovoltaic (PV) forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (26)

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