Abstract
Forecasting solar power with good precision is necessary for ensuring the reliable and economic operation of electricity grid. In this paper, we consider the task of predicting a given day photovoltaic power (PV power) outputs in 30 min intervals from previous solar power and weather data. We proposed a method combining extreme learning machine and wavelet transform (WT-ELM), which build a separation prediction model for every moment using weather data and corresponding PV power, the weather characteristics are treated as input features and the PV power data are the corresponding ground truth. In addition, we also compared our method with K Nearest Neighbour (K-NN) and support vector machine (SVM) based on clustering using the same data. Then we evaluated the performance of our approach for all data with different time interval. The results show that our result performs much more better than KNN based on clustering.
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