A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction

Guowei Cai, Wenjin Wang and Junhai Lu
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Guowei Cai: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Wenjin Wang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Junhai Lu: State Grid LiaoNing Electric Power Supply Co. Ltd., Shenyang 110000, China

Energies, 2016, vol. 9, issue 12, 1-19

Abstract: 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: short term load forecasting (STLF); support vector regression (SVR); artificial bee colony (ABC); seasonal autoregressive integrated moving average (SARIMA) (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 (7)

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