A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting
| dc.contributor.author | Li, Zhaoxuan | |
| dc.contributor.author | Rahman, SM Mahbobur | |
| dc.contributor.author | Vega, Rolando | |
| dc.contributor.author | Dong, Bing | |
| dc.date.accessioned | 2021-04-19T14:59:25Z | |
| dc.date.available | 2021-04-19T14:59:25Z | |
| dc.date.issued | 2016-01-19 | |
| dc.date.updated | 2021-04-19T14:59:26Z | |
| dc.description.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. | |
| dc.description.department | Mechanical Engineering | |
| dc.identifier | doi: 10.3390/en9010055 | |
| dc.identifier.citation | Energies 9 (1): 55 (2016) | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/357 | |
| dc.rights | Attribution 4.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | artificial neural network (ANN) | |
| dc.subject | support vector regression (SVR) | |
| dc.subject | photovoltaic (PV) forecasting | |
| dc.title | A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting | |
| dc.type | Article |