Ensemble-based Solar Power Prediction System Using Missing Value Interpolation Algorithm

Abstract

Environmental problems such as global warming due to excessive use of fossil fuels are becoming serious. In order to solve this problem, the supply of new and renewable energy is being activated, and the new and renewable energy market is also expanding. In particular, the share of solar and wind energy among new and renewable energies is rapidly increasing. However, uncertainty and volatility are inherent in renewable energy due to the characteristics of power generation that depend on natural conditions. This leads to a problem in which errors occur in the prediction of the amount of reserve energy required to secure the amount and cost of renewable energy generation. In this paper, we propose an ensemble-based solar power generation prediction system applying missing value interpolation algorithm. It predicts the amount of solar power generation by using weather forecast data from the Korea Meteorological Administration, and provides visualization and scheduling functions for the amount of power generation and predicted amount through a web page.