文献类型：期刊文献

英文题名：Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

作者：Sun, Wei[1];He, Yujun[2];Chang, Hong[3]

机构：[1]North China Elect Power Univ, Sch Econ & Management, Baoding 071003, Hebei, Peoples R China;[2]North China Elect Power Univ, Dept Elect & Commun Engn, Baoding 071003, Hebei, Peoples R China;[3]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200240, Peoples R China

年份：2015

卷号：8

期号：2

起止页码：939

外文期刊名：ENERGIES

收录：;EI(收录号：20151200651255);WOS:【SCI-EXPANDED(收录号:WOS:000353556700001)】；

基金：This work was supported by "Philosophy and Social Science Research of Hebei Province", "Soft Science Research Base of Hebei Province" and "the Fundamental Research Funds for the Central Universities (12MS137)".

语种：英文

外文关键词：Quantum computers - Backpropagation - Energy utilization - Quantum theory - Support vector machines - Computation theory - System theory

摘要：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 sigma 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.