Solar Power Output Forecasting Using Artificial Neural Network

The solar power generated by photovoltaic modules depends on many parameters namely the solar radiation and the cell temperature as these variables affect the current and voltage provided by the modules. In addition, cable loses, conversion losses and cloud coverage can also affect the power output. In this work, we propose to build a deep learning model that will implicitly take all these parameters into account and provide us with a prediction of the output power generated by PV power plants installed. The Artificial Neural Network used takes as an input the solar radiation, ambient temperature and modules' temperature, and as target the solar power. The ANN model was trained in a first experiment to give an hourly prediction. The second one provides an entire day forecast. The obtained results are very promising and the predicted output power profile is in good agreement with the measured one.