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Prediction of Wind Speed for Electric Power with a Combined Model Based on Hodrick-Prescott Decomposition

Published: 11 October 2024 Publication History

Abstract

As an environmental-friendly and sustainable energy, wind energy gets great attention currently. However, the intermittency and randomness of wind make its speed prediction a challenging gap. This study firstly applied Hampel filter to correct wind speed data and compress coefficient to reconstruct time sequence in frequency domain. Then Hdrick-Prescott algorithm was used to decompose the time sequence so as to reduce intermittency and uncertainty. Finally we combined Approximate Radius Base Function (ARBF), Extreme Learning Machine(ELM) and Long short-term memory(LSTM) to build a Hybrid Model. The numerical results indicated that, compared with previous single methods, the Hybrid Model proposed in this paper improved prediction accuracy and reliability significantly.

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  1. Prediction of Wind Speed for Electric Power with a Combined Model Based on Hodrick-Prescott Decomposition

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    ICCBN '24: Proceedings of the 2024 12th International Conference on Communications and Broadband Networking
    July 2024
    221 pages
    ISBN:9798400717109
    DOI:10.1145/3688636
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 11 October 2024

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    Author Tags

    1. Approximate Radius Base Function
    2. Extreme Learning Machine
    3. Hdrick-Prescott
    4. LSTM

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