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Chaotic Particle Swarm Optimization Algorithm In A Support Vector Regression Electric Load Forecasting Model

机译：支持向量回归电力负荷预测模型的混沌粒子群优化算法

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摘要

Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. Recently, support vector regression (SVR), with nonlinear mapping capabilities of forecasting, has been successfully employed to solve nonlinear regression and time series problems. However, it is still lack of systematic approaches to determine appropriate parameter combination for a SVR model. This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model. The empirical results reveal that the proposed model outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA). Finally, it also provides the theoretical exploration of the electric load forecasting support system (ELFSS).
机译：电力负荷的准确预测一直是电力工业中最重要的问题，特别是对于发展中国家而言。由于各种影响，电力负荷预测显示出高度非线性的特征。最近，具有预测的非线性映射功能的支持向量回归（SVR）已成功用于解决非线性回归和时间序列问题。但是，仍然缺乏确定SVR模型的适当参数组合的系统方法。这项研究阐明了应用混沌粒子群优化（CPSO）算法为SVR模型选择合适的参数组合的可行性。实证结果表明，所提出的模型在应用其他算法（遗传算法（GA）和模拟退火算法（SA））方面优于其他两个模型。最后，它还提供了电力负荷预测支持系统（ELFSS）的理论探索。

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