A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting
A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting
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DOI：
10.1016/j.epsr.2008.02.009
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发表时间：
2008-10-01
影响因子：
3.9
通讯作者：
Qi, Liang-bo
中科院分区：
文献类型：
--
作者：
Wang, Bo;Tai, Neng-ling;Qi, Liang-bo
本文提出了一种基于进化算法和粒子群优化的短期负荷预测新ARMAX模型。自回归（AR）和带有外生变量的移动平均（MA）（ARMAX）在负荷预测领域得到了广泛的应用。由于电力系统负荷的非线性特性，预测函数存在多个局部最优点。传统的基于梯度搜索的方法可能会陷入局部最优点并导致较高的误差。而基于进化算法和粒子群优化的混合方法可以比传统方法更有效地解决这个问题。它利用进化策略加速粒子群优化（PSO）的收敛速度，并应用遗传算法的交叉操作来增强全局搜索能力。基于华东地区市场的负荷数据对新的短期负荷预测ARMAX模型进行了测试，结果表明该方法取得了良好的预测精度。 (C) 2008 Elsevier B.V. 保留所有权利。
In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (C) 2008 Elsevier B.V. All rights reserved.