RECENT PROGRESS IN SHORT-TERM FORECASTING OF WIND ENERGY BASED ON STATISTICAL MODELS

Zhao Zeni; Yun Sining; Jia Lingyun; Shi Jiarong; He Ning; Yang Liu

doi:10.19912/j.0254-0096.tynxb.2021-0500

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (11) : 224-234. DOI: 10.19912/j.0254-0096.tynxb.2021-0500

RECENT PROGRESS IN SHORT-TERM FORECASTING OF WIND ENERGY BASED ON STATISTICAL MODELS

Zhao Zeni¹, Yun Sining¹, Jia Lingyun¹, Shi Jiarong², He Ning³, Yang Liu⁴

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Abstract

Considering the low prediction performances achieved by statistical models, various hybrid models have been proposed. By combining data preprocessing and optimization algorithms with basic statistical models or integrating artificial neural networks, convolutional neural networks, and recurrent neural networks, researchers can significantly improve the performance of short-term forecasting of wind energy.

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wind power / machine learning / forecasting / data processing / hybrid systems

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Zhao Zeni, Yun Sining, Jia Lingyun, Shi Jiarong, He Ning, Yang Liu. RECENT PROGRESS IN SHORT-TERM FORECASTING OF WIND ENERGY BASED ON STATISTICAL MODELS[J]. Acta Energiae Solaris Sinica. 2022, 43(11): 224-234 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0500

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