LAPSE:2018.0823
Published Article
LAPSE:2018.0823
Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm
November 16, 2018
This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS) and global harmony search algorithm (GHSA) with least squares support vector machines (LSSVM), namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS) algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA) model and other algorithms hybridized with LSSVM including genetic algorithm (GA), particle swarm optimization (PSO), harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.
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Keywords
electric load forecasting, fuzzy c-means (FCM), fuzzy time series (FTS), global harmony search algorithm (GHSA), least squares support vector machine (LSSVM)
Subject
Suggested Citation
Chen YH, Hong WC, Shen W, Huang NN. Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm. (2018). LAPSE:2018.0823
Author Affiliations
Chen YH: School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Hong WC: School of Economics & Management, Nanjing Tech University, Nanjing 211800, China; Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Road, Panchiao, Taipei 220, Taiwan [ORCID]
Shen W: School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Huang NN: School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China
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Hong WC: School of Economics & Management, Nanjing Tech University, Nanjing 211800, China; Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Road, Panchiao, Taipei 220, Taiwan [ORCID]
Shen W: School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Huang NN: School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China
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Journal Name
Energies
Volume
9
Issue
2
Article Number
E70
Year
2016
Publication Date
2016-01-26
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9020070, Publication Type: Journal Article
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Published Article
LAPSE:2018.0823
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doi:10.3390/en9020070
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[v1] (Original Submission)
Nov 16, 2018
Verified by curator on
Nov 16, 2018
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https://psecommunity.org/LAPSE:2018.0823
Original Submitter
Calvin Tsay
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