Exploring improvements of wind power forecasts using Convolutional Neural Networks and Time Series Analysis

Abstract: Due to environmental considerations, volumes of renewable power production are rapidly growing, and its share of the energy pool is increasing. The inter- mittent nature of wind power, being one of the main renewable energy sources, is a challenge when generating production forecasts. Accurate forecasts are nec- essary for the electrical grid to be kept in balance as the development of wind power continues. Power traders have a great incentive in achieving low forecast errors to decrease their imbalance costs. The basics of the energy market and wind power predictions are presented in this thesis. Thereafter, the possibilities of improving medium-term wind power forecasts are explored. Two separate models, based on the Kalman filter and Convolutional Neural Networks are implemented on historical production data with an existing black box model as a baseline comparison. The results are varying, and a conclusion is drawn that adding real-time production data as an input into the models can be beneficiary for the accuracy.