Abstract
With the introduction of the power generation prediction system, research is being conducted to predict hourly solar power generation through various algorithms and reduce prediction errors. However, increasing settlement revenue when participating in the prediction market is more important than improving prediction accuracy. In this study, we propose a method for predicting solar power generation using forecast and predicted weather data. In addition, the clustering algorithm was used based on solar radiation forecast data, and the causes of low prediction accuracy and profitability were analyzed for each cluster. Through this study, participation in the renewable energy generation prediction market is expected to be activated and opportunities for various business models will be provided.
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