Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

Weide Li, Xuan Yang, Hao Li and Lili Su
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Weide Li: School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China
Xuan Yang: School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China
Hao Li: School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China
Lili Su: School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China

Energies, 2017, vol. 10, issue 1, 1-17

Abstract: Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD), seasonal adjustment (S), cross validation (C), general regression neural network (GRNN) and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR). The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW) and Victorian State (VIC) in Australia). Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

Keywords: electricity demand forecasting; ensemble empirical mode decomposition (EEMD); generalized regression neural network (GRNN); support vector machine (SVM) (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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