Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm

Abstract Due to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests...

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Autores principales:	Rahmad Syah, Mohammad Rezaei, Marischa Elveny, Meysam Majidi Nezhad, Dadan Ramdan, Mehdi Nesaht, Afshin Davarpanah
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/743be6b7535d42ff85e91a2646215aa0
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spelling	oai:doaj.org-article:743be6b7535d42ff85e91a2646215aa02021-12-02T16:38:48ZDay-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm10.1038/s41598-021-96501-62045-2322https://doaj.org/article/743be6b7535d42ff85e91a2646215aa02021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96501-6https://doaj.org/toc/2045-2322Abstract Due to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests an algorithm for electricity price anticipation. To cover this goal, separate an algorithm into three steps, namely; pre-processing, learning and tuning. The pre-processing part consists of Wavelet Packet Transform (WPT) to analyze price signal to high and low frequency subseries and Variational Mutual Information (VMI) to select valuable input data in order to helps the learning part and decreases the computation burden. Owing to the learning part, a new Least squares support vector machine based self-adaptive fuzzy kernel (LSSVM-SFK) is proposed to extract best map pattern from input data. A new modified HBMO is introduced to optimally set LSSVM-SFK variables such as bias, weight, etc. To improve the performances of HBMO, two modifications are proposed that has high stability in HBMO. Suggested forecasting algorithm is examined on electricity markets that has acceptable efficiency than other models.Rahmad SyahMohammad RezaeiMarischa ElvenyMeysam Majidi NezhadDadan RamdanMehdi NesahtAfshin DavarpanahNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-21 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rahmad Syah Mohammad Rezaei Marischa Elveny Meysam Majidi Nezhad Dadan Ramdan Mehdi Nesaht Afshin Davarpanah Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm
description	Abstract Due to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests an algorithm for electricity price anticipation. To cover this goal, separate an algorithm into three steps, namely; pre-processing, learning and tuning. The pre-processing part consists of Wavelet Packet Transform (WPT) to analyze price signal to high and low frequency subseries and Variational Mutual Information (VMI) to select valuable input data in order to helps the learning part and decreases the computation burden. Owing to the learning part, a new Least squares support vector machine based self-adaptive fuzzy kernel (LSSVM-SFK) is proposed to extract best map pattern from input data. A new modified HBMO is introduced to optimally set LSSVM-SFK variables such as bias, weight, etc. To improve the performances of HBMO, two modifications are proposed that has high stability in HBMO. Suggested forecasting algorithm is examined on electricity markets that has acceptable efficiency than other models.
format	article
author	Rahmad Syah Mohammad Rezaei Marischa Elveny Meysam Majidi Nezhad Dadan Ramdan Mehdi Nesaht Afshin Davarpanah
author_facet	Rahmad Syah Mohammad Rezaei Marischa Elveny Meysam Majidi Nezhad Dadan Ramdan Mehdi Nesaht Afshin Davarpanah
author_sort	Rahmad Syah
title	Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm
title_short	Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm
title_full	Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm
title_fullStr	Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm
title_full_unstemmed	Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm
title_sort	day-ahead electricity price forecasting using wpt, vmi, lssvm-based self adaptive fuzzy kernel and modified hbmo algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/743be6b7535d42ff85e91a2646215aa0
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Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm

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