A Solar Power Prediction Using Support Vector Machines Based on Multi-source Data Fusion

Abstract

Photovoltaic (PV) power installed capacity of China has been growing rapidly with marked improvements in renewable energy accommodation during recent years. it is necessary to improve the accuracy of power forecasts, therefore the underlying electrical grid can be operated in a cost efficient way. This paper aims to improving the accuracy of short-term PV power predictions. Firstly, Measured power data, satellite-based data and numerical weather prediction data are utilized. The data sets of these sources are preprocessed and fused with machine learning techniques to get the sequence feature information. In particular, support vector machine based on data fusion (SVM-DF) is proposed to run as the main regression model. SVM-DF is an extension of the support vector machine and is capable of learning regression functions in continuous space by identifying structures in the mapping of input to output data. One advantage over Artificial Neural Network (ANN) is its ability to construct non-linear dependencies between the input and output data sets. Compared with predicting the PV power through classical ANN model, the SVM-DF approach acquire a more accurate dataset. Predictions of the higher quality can be achieved by SVM-DF with access to PV measurements and weather forecasts. To study further possible improvements in prediction quality, a study on NWP weather parameters, is performed to evaluate their suitability as input features for PV power forecasting. The results confirm the importance of data fusion which make use of spatio-temporal correlations between stations. Results showed that the SVM-DF model performed better than ANN model.