Use of Multilinear Adaptive Regression Splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany

The development of accurate forecasting methods for renewable energy sources can act as an important tool to integrate renewable power systems in the electricity grid. This paper proposes a technique that can forecast the power production of a photovoltaic plant one day in advance. The procedure is based on a regression model that considers the weather forecasts of the US Global Forecasting Service (GFS) as inputs, and it is trained and tested on a year of power production data of a 1.3 MW plant located in Borkum, Germany. The Multilinear Adaptive Regression Splines method was used to automatically define a reasonably simple model for the system with regression coefficients that could be easily interpreted. The results indicated that the forecasted power obtained by the model exhibited a high correlation with the measured data and relatively low errors despite the limited number of features that were included in the model and a low number of training samples. (C) 2017 Elsevier Ltd. All rights reserved.