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25.11.2023 | Research

A novel method for predicting wind speed using data decomposition-based reformer model

verfasst von: Bala Saibabu Bommidi, Kiran Teeparthi

Erschienen in: Earth Science Informatics | Ausgabe 1/2024

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Abstract

Wind energy has gained attention as an alternative to fossil fuels due to its effectiveness as a renewable energy source. The precise and reliable prediction of wind speed is crucial for the effective utilization and harnessing of wind power. This study proposes a novel hybrid methodology for wind speed prediction (WSP) by combining improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a memory-efficient Reformer model. This hybrid methodology is proposed to improve the accuracy of WSP. This approach involves decomposing the input data into multiple sub-series using the intrinsic mode function (IMF) obtained from the ICEEMDAN algorithm. The resulting sub-series are then processed through a Reformer (REF) model to predict wind speed. The verification of the efficiency and progression of the hybrid model that is proposed is conducted through the utilization of wind speed data obtained from two distinct wind farms. The current WSP methods exhibit a decline in performance as the time ahead increases. Therefore, this study addresses the viability of the proposed model by examining six different time horizons: 5-minutes, 10-minutes, 15-minutes, 30-minutes, 1-hour, and 2-hours. For evaluating the efficiency of proposed model for WSP, ten individual WSP models and ten hybrid WSP models are used for the comparative analysis. Based on the results of the experiments and comparative analysis, it has been observed that the hybrid model proposed in this study exhibits superior performance compared to other models across all time horizons while simultaneously preserving memory efficiency.

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Titel: A novel method for predicting wind speed using data decomposition-based reformer model
verfasst von: Bala Saibabu Bommidi
Kiran Teeparthi
Publikationsdatum: 25.11.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 1/2024
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-01123-3

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