publication . Article . Other literature type . 2016

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

Name: A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting
Keywords: artificial neural network (ANN), support vector regression (SVR), photovoltaic (PV) forecasting, Technology, T

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,

Publisher: MDPI AG

Summary

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 ...

doi: 10.3390/en9010055

Subjects

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

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