Fan Xiao¹, Xiong Ping¹, Yeyang Li^2,*, Yusen Xu², Yiqun Kang¹, Dan Liu¹, Nianming Zhang¹

Energy Engineering, Vol.121, No.2, pp. 359-376, 2024, DOI:10.32604/ee.2023.040887

Abstract The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale. Therefore, wind power forecasting plays a key role in improving the safety and economic benefits of the power grid. This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data. Based on the graph attention network and attention mechanism, the method extracts spatial-temporal characteristics from the data of multiple wind farms. Then, combined with a deep neural network, a convolutional graph… More >

Zahraa Tarek¹, Mahmoud Y. Shams^2,*, Ahmed M. Elshewey³, El-Sayed M. El-kenawy^4,5, Abdelhameed Ibrahim⁶, Abdelaziz A. Abdelhamid^7,8, Mohamed A. El-dosuky^1,9

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 715-732, 2023, DOI:10.32604/cmc.2023.032533

Abstract Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and… More >