Wind power generation prediction based on weather forecast data using deep neural networks

Wind power generation is one of the most important renewable energy sour-ces. Although predicting the amount of power generation is crucial for efficient opera-tions, it is not easy because of fluctuating nature of wind speed. This paper applies a deep neural network method to predicting wind power generation based on weather forecast da-ta. Wind power generation data were collected from a power plant located in Jeju, South Korea, and weather forecast data for the nearby weather stations were collected. The prediction performance of the model was evaluated with wind power generation data and weather forecast in terms of root mean square error, mean square error, mean absolute error, and R-squared.