Wind power forcasting based on hybrid CEEMDAN-EWT deep learning method

Karijadi, Irene, Chou, Shuo-Yan and Dewabharata, Anindhita (2023) Wind power forcasting based on hybrid CEEMDAN-EWT deep learning method. Renewable Energy, 218 (119357). pp. 1-13. ISSN pISSN: 0960-1481 eISSN: 1879-0682

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Abstract

A precise wind power forecast is required for the renewable energy platform to function effectively. By having a precise wind power forecast, the power system can better manage its supply and ensure grid reliability. However, the nature of wind power generation is intermittent and exhibits high randomness, which poses a challenge to obtaining accurate forecasting results. In this study, a hybrid method is proposed based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Empirical Wavelet Transform (EWT), and deep learning-based Long Short-Term Memory (LSTM) for ultra-short-term wind power forecasting. A combination of CEEMDAN and EWT is used as the preprocessing technique, where CEEMDAN is first employed to decompose the original wind power data into several subseries, and the EWT denoising technique is used to denoise the highest frequency series generated from CEEMDAN. Then, LSTM is utilized to forecast all the subseries from the CEEMDAN-EWT process, and the forecasting results of each subseries are aggregated to achieve the final forecasting results. The proposed method is validated on real-world wind power data in France and Turkey. Our experimental results demonstrate that the proposed method can forecast more accurately than the benchmarking methods.

Item Type:	Article
Uncontrolled Keywords:	Wind power Forecasting Deep learning Long short term memory Data preprocessing Artificial Intelligence
Subjects:	Engineering > Industrial Engineering
Divisions:	Journal Publication
Depositing User:	F.X. Hadi
Date Deposited:	20 Nov 2024 03:46
Last Modified:	18 Feb 2025 02:48
URI:	https://repositori.ukwms.ac.id/id/eprint/41169

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