Design of a Wind Power Forecasting System Based on Deep Learning

Abstract In recent years, wind energy, as a ubiquitous, easy-to-capture cost-effective clean energy, is accounting for a sharp increase in the installed capacity of China’s new energy grid. However, its stochastic volatility has always brought challenges to the generation scheduling of wind farms. To better optimize the energy management level of wind farms and improve the stability of wind power grid connection, this paper proposes a design of a wind power forecasting system based on deep learning. Our system architecture mainly includes data pre-processing, power prediction, and data application modules. The data pre-processing module will correct the numerical weather forecast (NWP) updated twice daily to get the wind turbine generator (WTG) hub height weather forecast and resample the SCADA data in a 15-minute time resolution. The power prediction module will periodically perform the ultra-short-term and short-term forecasting results and record them in the database. On one hand, the data application module will present the power prediction results and SCADA data to the web page for display. On the other hand, it will provide data to the energy dispatching department for reference. The experiment shows the effect of using a residual channel attention network (RCAN) to correct the NWP and the influence of two different RNN cores, including GRU and LSTM, on the prediction of ultra-short-term wind power results of the DeepAR model. The experimental results show that the proposed RCAN can effectively correct the results of the numerical weather forecast to a single WTG, and the DeepAR model using the GRU core can achieve better performance in the test set than the DeepAR model using the LSTM core. Thus, we choose GRU as the core of the model of our power prediction module.