SHORT-TERM POWER FORECASTING BY STATISTICAL METHODS FOR PHOTOVOLTAIC PLANTS IN SOUTH ITALY |
|---|
| Maria Grazia De Giorgi, Paolo Maria Congedo, Maria Malvoni, Marco Tarantino |
- Abstract:
- Statistical methods based on Multiregression Analysis and Artificial Neural Networks (ANNs) have been developed in order to predict power production of a 960 kWp grid-connected photovoltaic (PV) plant in the campus of the University of Salento, Italy.
The neural network has been used only as a statistic model based on time series of PV power and meteorological variables, as module temperature, ambient temperature and irradiance on module’s plain. In particular, a sensitivity analysis has been carried out in order to find those weather parameters with the best impact on the forecasting. - Keywords:
- forecasting, photovoltaic power, Artificial Neural Networks, prediction, Multiregression Analysis
- Download:
- IMEKO-TC19-2013-034.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC19
- Event name:
- Protecting Environment, Climate Changes and Pollution Control
- Title:
- 4th Symposium on Environmental Instrumentation and Measurements
- Place:
- Lecce, ITALY
- Time:
- 03 June 2013 - 04 June 2013