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.<br />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

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IMEKO-TC19-2013-034.pdf

DOI

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IMEKO TC

TC19 - Environmental Measurements

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