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Information 2017, 8(4), 120; doi:10.3390/info8040120

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

School of Economics and Management, North China Electric Power University, Beijing 102206, China
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Author to whom correspondence should be addressed.
Academic Editor: Günter Neumann
Received: 31 August 2017 / Revised: 21 September 2017 / Accepted: 25 September 2017 / Published: 28 September 2017
(This article belongs to the Section Artificial Intelligence)
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Abstract

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. View Full-Text
Keywords: electricity price forecasting; least square support vector machine (LSSVM); Beveridge-Nelson decomposition method (BND); fruit fly optimization algorithm (FOA); intelligence forecasting electricity price forecasting; least square support vector machine (LSSVM); Beveridge-Nelson decomposition method (BND); fruit fly optimization algorithm (FOA); intelligence forecasting
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Guo, W.; Zhao, Z. A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting. Information 2017, 8, 120.

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