Zhaowei Yang¹, Bo Yang^2,*, Wenqi Liu¹, Miwei Li², Jiarong Wang², Lin Jiang³, Yiyan Sang⁴, Zhenning Pan⁵

Energy Engineering, Vol.122, No.2, pp. 405-430, 2025, DOI:10.32604/ee.2025.059515 - 31 January 2025

Abstract Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids. Although numerous studies have employed various methods to forecast wind power, there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction. To improve the accuracy of short-term wind power forecast, this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer, which is based on seasonal and trend decomposition using LOESS (STL) and iTransformer model optimized by improved arithmetic optimization algorithm (IAOA).… More >

Ming Zhang, Hongbo Li, Xing Deng^*

Energy Engineering, Vol.119, No.1, pp. 237-252, 2022, DOI:10.32604/EE.2022.017916 - 22 November 2021

Abstract The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To More >

Xing Deng^1,2, Haijian Shao^1,2,*

Energy Engineering, Vol.117, No.5, pp. 279-287, 2020, DOI:10.32604/EE.2020.011619 - 07 September 2020

Abstract Recurrent neural networks (RNNs) as one of the representative deep learning methods, has restricted its generalization ability because of its indigestion hidden-layer information presentation. In order to properly handle of hidden-layer information, directly reduce the risk of over-fitting caused by too many neuron nodes, as well as realize the goal of streamlining the number of hidden layer neurons, and then improve the generalization ability of RNNs, the hidden-layer information of RNNs is precisely analyzed by using the unsupervised clustering methods, such as Kmeans, Kmeans++ and Iterative self-organizing data analysis (Isodata), to divide the similarity of More >