DocumentCode :
476281
Title :
High-order adaptive model to forecast regional electricity loads
Author :
Chen, Yao-Hsien ; Liu, Jing-Wei ; Cheng, Chin-Hsue
Volume :
6
fYear :
2008
fDate :
12-15 July 2008
Firstpage :
3277
Lastpage :
3282
Abstract :
Over the past few years, a considerable number of studies have been proposed on load forecasting. This paper aims at proposing a promising model using high-order adaptive fuzzy time-series algorithm to get more efficient forecasting. From the reviewed literature related to fuzzy time-series, there are two points need to be concerned. The first is to determine a reasonable universe of discourse and the length of intervals, and the second is many researchers ignore the information of trend patterns change in the past history. Hence, this paper utilized the trend weighted and high order adaptive model to deal with above drawbacks. The proposed model is applied for forecasting the regional electricity load in Taiwan. The experiment results showed that the proposed model outperforms the listing methods under MAPE (mean absolute percentage error) criteria.
Keywords :
fuzzy set theory; load forecasting; time series; high-order adaptive fuzzy time-series algorithm; high-order adaptive model; mean absolute percentage error; regional electricity loads forecasting; Cybernetics; Economic forecasting; Information management; Load forecasting; Load modeling; Machine learning; Power system economics; Power system modeling; Power system reliability; Predictive models; Fuzzy time-series; adaptive expectation model; linguistic variable; load forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
Type :
conf
DOI :
10.1109/ICMLC.2008.4620971
Filename :
4620971
Link To Document :
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