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Energies 2015, 8(2), 939-959; doi:10.3390/en8020939

Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

1,* , 2
and
3,*
1
School of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China
2
Department of Electronic & Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, China
3
Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200240, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Vincenzo Dovì
Received: 21 November 2014 / Revised: 12 January 2015 / Accepted: 13 January 2015 / Published: 28 January 2015
(This article belongs to the Special Issue Energy Policy and Climate Change)
View Full-Text   |   Download PDF [531 KB, uploaded 17 March 2015]   |  

Abstract

Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM) energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1) (GM (1, 1)), back propagation (BP) and LSSVM, in terms of prediction accuracy and forecasting risk. View Full-Text
Keywords: fossil fuel energy forecasting; power generation; LSSVM; quantum harmony search algorithm (QHSA) fossil fuel energy forecasting; power generation; LSSVM; quantum harmony search algorithm (QHSA)
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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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Sun, W.; He, Y.; Chang, H. Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model. Energies 2015, 8, 939-959.

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