| Full text | |
| Author(s): |
Lopes, Sofia M. A.
;
Cari, Elmer P. T.
;
Hajimirza, Shima
Total Authors: 3
|
| Document type: | Journal article |
| Source: | JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME; v. 144, n. 2, p. 11-pg., 2022-04-01. |
| Abstract | |
The inclusion of photovoltaic systems in distribution networks has raised the importance of the prediction of photovoltaic power for safe planning and operation. Artificial neural networks (ANNs) have been used in this task due to its capacity of representing nonlinearities. However, the profile of the data used may affect the forecast accuracy. This manuscript reports on a comparative analysis of the performance of four neural network models for photovoltaic power forecast regarding their input dataset. Four sets composed of photovoltaic power data (local measurements) and external weather data (remote measurements) were used, and the networks were validated through actual measurements from a photovoltaic micro plant. The ANN that dealt with only weather data showed a good level of accuracy, being a useful tool for the feasibility analysis of new photovoltaic projects. In addition, the approach that used only photovoltaic power data has excelled and can be used in electric sector companies. (AU) | |
| FAPESP's process: | 17/50389-2 - Optimization and prediction modeling of solar module considering environmental parameters |
| Grantee: | Elmer Pablo Tito Cari |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 17/09208-4 - Hibrid Method for parameter estimation of photovoltaic power plants |
| Grantee: | Elmer Pablo Tito Cari |
| Support Opportunities: | Regular Research Grants |