Short-Term Electricity Price Forecasting Based on Nonparametric GARCH Residuals Correction-Least Square Support Vector Machine

Huang, Yuansheng; Shenhai, Huang; Hu, Yin-Ping

doi:10.1142/9789813202689_0006

Cited by 1 publication

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“…For the conventional time-series statistical method, it holds the assumption that the electricity price has linear relationship with its influencing factors. This kind of forecasting technique mainly includes the auto-regressive integrated moving average (ARIMA) [6][7][8] method and generalized autoregressive conditional heteroscedasticity (GARCH) [9][10][11]. However, in fact, the relationships between electricity price and its influencing factors are not linear.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting

Guo

Zhao

2017

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Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND) method, fruit fly optimization algorithm (FOA), and least square support vector machine (LSSVM) model, namely BND-FOA-LSSVM model. Firstly, the original electricity price time series were decomposed into deterministic term, periodic term, and stochastic term by using BND model. Then, these three decomposed terms were forecasted by employing LSSVM model, respectively. Meanwhile, to improve the forecasting performance, a new swarm intelligence optimization algorithm FOA was used to automatically determine the optimal parameters of LSSVM model for deterministic term forecasting, periodic term forecasting, and stochastic term forecasting. Finally, the forecasting result of electricity price can be obtained by multiplying the forecasting values of these three terms. The results show the mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed BND-FOA-LSSVM model are respectively 3.48%, 11.18 Yuan/MWh and 9.95 Yuan/MWh, which are much smaller than that of LSSVM, BND-LSSVM, FOA-LSSVM, auto-regressive integrated moving average (ARIMA), and empirical mode decomposition (EMD)-FOA-LSSVM models. The proposed BND-FOA-LSSVM model is effective and practical for electricity price forecasting, which can improve the electricity price forecasting accuracy.

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