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Energies 2016, 9(2), 70; doi:10.3390/en9020070

Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

1
School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China
2
School of Economics & Management, Nanjing Tech University, Nanjing 211800, China
3
Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Road, Panchiao, Taipei 220, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Sukanta Basu
Received: 19 October 2015 / Revised: 8 December 2015 / Accepted: 21 January 2016 / Published: 26 January 2016
View Full-Text   |   Download PDF [514 KB, uploaded 26 January 2016]   |  

Abstract

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. View Full-Text
Keywords: electric load forecasting; least squares support vector machine (LSSVM); global harmony search algorithm (GHSA); fuzzy time series (FTS); fuzzy c-means (FCM) electric load forecasting; least squares support vector machine (LSSVM); global harmony search algorithm (GHSA); fuzzy time series (FTS); fuzzy c-means (FCM)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chen, Y.H.; Hong, W.-C.; Shen, W.; Huang, N.N. Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm. Energies 2016, 9, 70.

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