- Title
- Wind-speed Forecasting based on Smoothing Ensemble Empirical Mode Decomposition and LSTM
- Creator
- Bahri, Mona; Vahidnia, Sahand; Ghignone, Leo
- Relation
- 2023 International Conference on Power and Renewable Energy Engineering (PREE). Proceedings of the 2023 International Conference on Power and Renewable Energy Engineering, PREE 2023 (Tokyo, Japan 20-22 October, 2023) p. 107-110
- Publisher Link
- http://dx.doi.org/10.1109/PREE57903.2023.10370420
- Publisher
- Institute of Electrical and Electronic Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2023
- Description
- One of the main challenges in power generation in wind farms is to forecast wind accurately. This is due to the non-stationary and non-linearity characteristics of wind data. These characteristics make it difficult for the normal statistical methods and the common statistical and computational intelligence methods to provide adequate predictions for wind speed. Empirical Mode Decomposition (EMD) is designed to de-compose non-stationary and non-linear data into their embedded components. In this study, we used a hybrid method composed of an improved variation of EMD, Smoothing Ensemble Empirical Mode Decomposition (SEEMD), and long-short-term memory neural networks (LSTM) to predict wind data. The results of this show that the proposed method provides better forecasting compared to the existing ones.
- Subject
- wind forecasting; time series; machine learning; Empirical Mode Decomposition (EMD); Smoothing Ensemble Empirical Mode Decomposition (SEEMD); SDG 7; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1504145
- Identifier
- uon:55462
- Identifier
- ISBN:9798350321906
- Language
- eng
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