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Long term electric load forecasting based on particle swarm optimization

机译:基于粒子群算法的长期电力负荷预测

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This paper presents a new method for annual peak load forecasting in electrical power systems. The problem is formulated as an estimation problem and presented in state space form. A particle swarm optimization is employed to minimize the error associated with the estimated model parameters. Actual recorded data from Kuwaiti and Egyptian networks are used to perform this study. Results are reported and compared to those obtained using the well known least error squares estimation technique. The performance of the proposed method is examined and evaluated. Finally, estimated model parameters are used in forecasting the annual peak demands of Kuwait network.
机译:本文提出了一种电力系统年度峰值负荷预测的新方法。该问题被公式化为估计问题,并以状态空间形式表示。采用粒子群优化来最小化与估计的模型参数相关的误差。来自科威特和埃及网络的实际记录数据用于进行这项研究。报告结果并将其与使用众所周知的最小误差平方估计技术获得的结果进行比较。对提出的方法的性能进行了检查和评估。最后,将估计的模型参数用于预测科威特网络的年度峰值需求。

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