Conference article

This paper presents a model for predicting the next-day energy production of a photovoltaic solar plant. The model is capable of forecasting the next-day production profile of such a system; merely by using the information obtained from the plant itself and the solar global radiation values for the previous operation days. This prediction is key in many photovoltaic systems in order to interact with conventional electrical grids. For example; Spanish legislation requires this type of information for large photovoltaic plants. In fact; the deviations from the predicted values are financially penalized. A three-stage procedure is used to build the model; which is capable of learning specific information about each facility and of using this information to fit the prediction. This model binds the use of regression techniques and the use of a special type of probabilistic finite automata developed from machine learning. The energy prediction yearly error is less that 20 percent which is a significant improvement over previous proposed models; whose errors are around 25 percent.

Short term forecasting; photovoltaic energy production; machine learning