Short-term forecasting of power production in a large-scale photovoltaic plant

•Effective ANN-based models for forecasting the power produced by a LS-GCPV plant is presented.•A simple but accurate analytical expressions have been developed to forecast the power output.•The effectiveness of the split of the dataset into three different ones has been shown.•The better ability of ANN-models to forecast the power produced has been verified experimentally.

In this paper, a simple but accurate approach for short-term forecasting of the power produced by a Large-Scale Grid Connected Photovoltaic Plant (LS-GCPV) is presented. A 1-year database of solar irradiance, cell temperature and power output produced by a 1-MWp photovoltaic plant located in Southern Italy is used for developing three distinct artificial neural network (ANN) models, to be applied to three typical types of day (sunny, partly cloudy and overcast). The possibility of obtaining accurate results by using solely the monitored data rather than knowing the actual architecture and details of the plant is a notable advantage; in particular, the method’s reliability gives to operation and maintenance and to grid operators excellent confidence in the evaluation of the performance of the plant and in the programming of the dispatching plans, respectively.