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

Yan Hong Chen, Wei-Chiang Hong (), Wen Shen and Ning Ning Huang
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Yan Hong Chen: School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Wen Shen: School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Ning Ning Huang: School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China

Energies, 2016, vol. 9, issue 2, 1-13

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.

Keywords: electric load forecasting; least squares support vector machine (LSSVM); global harmony search algorithm (GHSA); fuzzy time series (FTS); fuzzy c-means (FCM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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