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.
SHORT-TERM POWER FORECASTING BY STATISTICAL METHODS FOR PHOTOVOLTAIC PLANTS IN SOUTH ITALY
DE GIORGI, Maria Grazia;CONGEDO, Paolo Maria;MALVONI, MARIA;TARANTINO, MARCO
2013-01-01
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.File in questo prodotto:
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