...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Electric power systems research >A New Armax Model Based On Evolutionary Algorithm And Particle Swarm Optimization For Short-term Load Forecasting
【24h】

A New Armax Model Based On Evolutionary Algorithm And Particle Swarm Optimization For Short-term Load Forecasting

机译：基于进化算法和粒子群算法的Armax短期负荷预测新模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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 foreca'sting 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.
机译：提出了一种基于进化算法和粒子群算法的ARMAX短期负荷预测模型。具有外生变量（ARMAX）的自回归（AR）和移动平均值（MA）已广泛应用于负荷预测领域。由于电力系统负载的非线性特征，因此，前馈功能具有许多局部最优点。基于梯度搜索的传统方法可能会陷入局部最优点，从而导致较高的误差。同时，基于进化算法和粒子群优化算法的混合方法比传统方法能够更有效地解决这一问题。它利用进化策略来加速粒子群优化（PSO）的收敛，并应用遗传算法的交叉运算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ARMAX短期负荷预测模型进行了测试，结果表明该方法取得了较好的精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号