The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection
Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least squa...
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ftdoajarticles:oai:doaj.org/article:1bf29834af084b96ab9849ce99ea2ffe 2023-05-15T18:26:31+02:00 The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection Jin-peng Liu Chang-ling Li 2017-07-01T00:00:00Z https://doi.org/10.3390/su9071188 https://doaj.org/article/1bf29834af084b96ab9849ce99ea2ffe EN eng MDPI AG https://www.mdpi.com/2071-1050/9/7/1188 https://doaj.org/toc/2071-1050 2071-1050 doi:10.3390/su9071188 https://doaj.org/article/1bf29834af084b96ab9849ce99ea2ffe Sustainability, Vol 9, Iss 7, p 1188 (2017) load forecasting least square support vector machine sperm whale algorithm feature selection Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 article 2017 ftdoajarticles https://doi.org/10.3390/su9071188 2022-12-31T14:16:56Z Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR) are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting. Article in Journal/Newspaper Sperm whale Directory of Open Access Journals: DOAJ Articles Sustainability 9 7 1188 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
load forecasting least square support vector machine sperm whale algorithm feature selection Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
spellingShingle |
load forecasting least square support vector machine sperm whale algorithm feature selection Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Jin-peng Liu Chang-ling Li The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection |
topic_facet |
load forecasting least square support vector machine sperm whale algorithm feature selection Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
description |
Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR) are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting. |
format |
Article in Journal/Newspaper |
author |
Jin-peng Liu Chang-ling Li |
author_facet |
Jin-peng Liu Chang-ling Li |
author_sort |
Jin-peng Liu |
title |
The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection |
title_short |
The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection |
title_full |
The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection |
title_fullStr |
The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection |
title_full_unstemmed |
The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection |
title_sort |
short-term power load forecasting based on sperm whale algorithm and wavelet least square support vector machine with dwt-ir for feature selection |
publisher |
MDPI AG |
publishDate |
2017 |
url |
https://doi.org/10.3390/su9071188 https://doaj.org/article/1bf29834af084b96ab9849ce99ea2ffe |
genre |
Sperm whale |
genre_facet |
Sperm whale |
op_source |
Sustainability, Vol 9, Iss 7, p 1188 (2017) |
op_relation |
https://www.mdpi.com/2071-1050/9/7/1188 https://doaj.org/toc/2071-1050 2071-1050 doi:10.3390/su9071188 https://doaj.org/article/1bf29834af084b96ab9849ce99ea2ffe |
op_doi |
https://doi.org/10.3390/su9071188 |
container_title |
Sustainability |
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9 |
container_issue |
7 |
container_start_page |
1188 |
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1766208486554730496 |