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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ftmdpi:oai:mdpi.com:/2071-1050/9/7/1188/ 2023-08-20T04:09:59+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 agris 2017-07-06 application/pdf https://doi.org/10.3390/su9071188 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/su9071188 https://creativecommons.org/licenses/by/4.0/ Sustainability; Volume 9; Issue 7; Pages: 1188 load forecasting least square support vector machine sperm whale algorithm feature selection Text 2017 ftmdpi https://doi.org/10.3390/su9071188 2023-07-31T21:09:41Z 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. Text Sperm whale MDPI Open Access Publishing Sustainability 9 7 1188 |
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English |
topic |
load forecasting least square support vector machine sperm whale algorithm feature selection |
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load forecasting least square support vector machine sperm whale algorithm feature selection 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 |
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 |
Text |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2017 |
url |
https://doi.org/10.3390/su9071188 |
op_coverage |
agris |
genre |
Sperm whale |
genre_facet |
Sperm whale |
op_source |
Sustainability; Volume 9; Issue 7; Pages: 1188 |
op_relation |
https://dx.doi.org/10.3390/su9071188 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/su9071188 |
container_title |
Sustainability |
container_volume |
9 |
container_issue |
7 |
container_start_page |
1188 |
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1774723843289513984 |