Design of a Wind Power Forecasting System Based on Deep Learning
نویسندگان
چکیده
Abstract In recent years, wind energy, as a ubiquitous, easy-to-capture cost-effective clean is accounting for sharp increase in the installed capacity of China’s new energy grid. However, its stochastic volatility has always brought challenges to generation scheduling farms. To better optimize management level farms and improve stability power grid connection, this paper proposes design forecasting system based on deep learning. Our architecture mainly includes data pre-processing, prediction, application modules. The pre-processing module will correct numerical weather forecast (NWP) updated twice daily get turbine generator (WTG) hub height resample SCADA 15-minute time resolution. prediction periodically perform ultra-short-term short-term results record them database. On one hand, present web page display. other it provide dispatching department reference. experiment shows effect using residual channel attention network (RCAN) NWP influence two different RNN cores, including GRU LSTM, DeepAR model. experimental show that proposed RCAN can effectively single WTG, model core achieve performance test set than LSTM core. Thus, we choose our module.
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ژورنال
عنوان ژورنال: Journal of physics
سال: 2023
ISSN: ['0022-3700', '1747-3721', '0368-3508', '1747-3713']
DOI: https://doi.org/10.1088/1742-6596/2562/1/012043