Numerical Weather Prediction Data Free Solar Power Forecasting with Neural Networks

Abstract

The worldwide increase in renewable energy penetration levels has made accuracy, availability, and affordability of wind and solar energy forecasting systems an integral part of the modern power grids. The present paper describes an approach to forecasting one-day-ahead photovoltaic (PV) power generation without the use of numerical weather prediction (NWP) data. The presented approach uses a closed loop non-linear autoregressive artificial neural network (CL-NAR-ANN) model with only the historical generated PV power data as input. In case of emergency, if the communication channel with the weather provider fails, the whole forecasting system runs a risk of failing. Also, purchasing NWP data might be too expensive for smaller utilities. In such situations, NWP data free models can provide cost-effective and reasonably accurate PV power forecasts, which can act as a good backup solution. Furthermore, the model is evaluated using a dataset from the Global Energy Forecasting Competition of 2014 (GEFCom14) and its results are compared to other data-driven models such as polynomial and artificial neural network (ANN) models with and without NWP data as input. The results suggest that the CL-NAR-ANN model delivers acceptable forecasts and outperforms other NWP free models by a margin of 8% in terms of root mean square error, hence supporting the possibility of obtaining acceptable forecasts using the CL-NAR-ANN.