LAPSE:2019.0305
Published Article
LAPSE:2019.0305
A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction
Guowei Cai, Wenjin Wang, Junhai Lu
February 27, 2019
In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches.
Keywords
artificial bee colony (ABC), seasonal autoregressive integrated moving average (SARIMA), short term load forecasting (STLF), support vector regression (SVR)
Suggested Citation
Cai G, Wang W, Lu J. A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction. (2019). LAPSE:2019.0305
Author Affiliations
Cai G: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Wang W: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Lu J: State Grid LiaoNing Electric Power Supply Co. Ltd., Shenyang 110000, China
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Journal Name
Energies
Volume
9
Issue
12
Article Number
E994
Year
2016
Publication Date
2016-11-25
Published Version
ISSN
1996-1073
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PII: en9120994, Publication Type: Journal Article
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LAPSE:2019.0305
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doi:10.3390/en9120994
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Feb 27, 2019
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Feb 27, 2019
 
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Calvin Tsay
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