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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...
We consider the task of predicting the half-hourly solar PhotoVoltaic (PV) power output for the next day from three sources: previous solar power, previous weather data and weather forecasts. We propose DWkNN, a data source weighed nearest neighbor method that considers the importance of the different data sources and learns the best weights for them. We evaluate its performance using Australian PV...
Accurate forecasting of solar power is needed for the successful integration of solar energy into the electricity grid. In this paper we consider the task of predicting the half-hourly solar photovoltaic power for the next day from previous solar power and weather data. We propose and evaluate several clustering based methods, that group the days based on the weather characteristics and then build...
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