publication . Article . Other literature type . 2016

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

Zhaoxuan Li; Rolando E. Vega; S.M. Mahbobur Rahman; Bing Dong;
Open Access
  • Published: 19 Jan 2016 Journal: Energies, volume 9, page 55 (eissn: 1996-1073, Copyright policy)
  • Publisher: MDPI AG
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 ...
free text keywords: artificial neural network (ANN), support vector regression (SVR), photovoltaic (PV) forecasting, Technology, T, Mean squared error, Mean absolute error, Machine learning, computer.software_genre, computer, Artificial neural network, Support vector machine, Mean percentage error, Artificial intelligence, business.industry, business, Engineering, Solar power, Production forecasting, Photovoltaic system
Energy Research
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Article . 2016
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Article . 2016
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