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.
Highlights
With economic and social development, more and more oil, coal, electricity power, natural gas and other energies are consumed
In order to highlight the advantages of proposed model ensemble empirical mode decomposition (EEMD)-SCGRNN-PSVR, three models are established to make a comparison in this research, i.e., cross validation for GRNN (CGRNN), empirical mode decomposition (EMD)-CGRNN-PSVR and EEMD-CGRNN-PSVR
This paper proposed a forecasting model named EEMD-SCGRNN-PSVR for one week ahead electricity demand forecasting
Summary
With economic and social development, more and more oil, coal, electricity power, natural gas and other energies are consumed. Taylor [6] used double seasonal exponential smoothing model for short-term electrical demand forecasting and obtained a good result. In the latest several years, researchers proposed various and novel hybrid models for increasing forecasting accuracy of nonlinear electric demand. Bouzerdoum et al [23] combined seasonal autoregressive integrated moving average model (ARIMA) and SVM to forecast the short-term power demand and showed that the developed hybrid model performed better than both the single models. Combined time series modeling with adaptive particle swarm optimization was proposed by Wang et al [25] This hybrid model includes S-ARIMA, exponential smoothing model and SVM. SVM methodology gives better forecasting accuracy for price time series; Another hybrid method which united wavelet transform and a combined forecast method is proposed by Amjady and Keynia [33].
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