A photovoltaic production estimator based on artificial neural networks

The ability to forecast the expected power production from renewable sources nowadays is increasingly critical because of the reliability expected from them. For instance improving the reliability of photovoltaic production forecasts in a small/medium microgrid permits to save money to supply its own loads and also to plan the participation to the ongoing services the smart distribution grid will require. In this paper we propose a method to predict photovoltaic production based on a statistical model. This type of models, compared to other ones, are easily configurable, cope well with heterogeneous plants, with different ageing devices and are able to consider diverse exogenous, well known and accidental, drawbacks. Such models is daily updated with the data arising from the monitoring until 5 days before and include the relevant variables for the photovoltaic forecasting function.