Hybrid demand model for load estimation and short term load forecasting in distribution electric systems

被引：56

作者：

Villalba, SA ^{[1
]}

Bel, CA ^{[1
]}

机构：

[1] Univ Politecn Valencia, Dept Elect Engn, E-46071 Valencia, Spain

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2000年 / 15卷 / 02期

关键词：

load demand models; artificial neural networks; state estimation; load forecasting;

D O I：

10.1109/61.853017

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A new Hybrid Demand Model to enhance load modeling in distribution applications is proposed in this paper. This model is specially well suited for the applications emerging from the new structure of the power sector worldwide. The modeling is performed in two steps. The first one is a state space model for load estimation at the selected points in the network,It uses information already available in the utility and also some measurements, and it suggests a measurement planning for meter location and bad data detection. The second step is an artificial neural network (ANN) model for short-term load forecasting which is able to cope with the nonlinear behavior of the load. The model has been validated in simulation studies and using historical data from the distribution level.

引用

页码：764 / 769

页数：6

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