Solar Power Prediction Using Weather Type Pair Patterns

Although solar power is one of the most widely used renewable energy sources, it is highly variable and needs accurate forecasting for its large-scale integration into the electricity grid. We propose WPP, a Weather type Pair Pattern approach, for directly and simultaneously predicting the solar power output for the next day at half-hourly intervals. WPP firstly partitions the days from the training data into clusters based on their weather characteristics and then uses the cluster label of the consecutive days to form pair patterns. A separate prediction model is built for each pair pattern group, which takes as an input the solar power output for the previous day and predicts the one for the next day. The performance of WPP is evaluated using two years of Australian data and compared with a number of state-of-the-art methods and baselines. The results show that WPP is the most accurate method, demonstrating the advantage of using the similarity between the weather characteristics of the consecutive days, in addition to clustering the days and building specialized prediction models for each group.