A Method to Predict the Intermittent Power by Classification Model

More and more power plants have been constructed and generated by intermittent energy. As a clean and renewable energy, such sources as wind and solar are favored in the new generation of power grid system. However, influenced by factors of geography, circumstance and climates, the renewable energy has the characteristics of intermittency, volatility and uncontrollability, which reduce the efficient utilization of intermittent energy. This paper uses data mining methods to predict the level of power generation from solar energy, by analyzing the information collected from distributed power plants. Investigating the real-world power grid dataset, the experimental result verifies the feasibility of the proposed method for improving the utilization of intermittent energy.