A Neural Network based Very Short Term Load Forecaster for the interim ISO New England Electricity Market System

被引：32

作者：

Shamsollahi, P ^{[1
]}

Cheung, KW ^{[1
]}

Chen, Q ^{[1
]}

Germain, EH ^{[1
]}

机构：

[1] ALSTOM ESCA Corp, Bellevue, WA 98004 USA

来源：

PICA 2001: 22ND IEEE POWER ENGINEERING SOCIETY INTERNATIONAL CONFERENCE ON POWER INDUSTRY COMPUTER APPLICATIONS | 2001年

关键词：

System Load Forecasting; Artificial Neural Networks; Electricity Market System;

D O I：

10.1109/PICA.2001.932351

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：

This paper presents the development and implementation of an Artificial Neural Network (ANN) based Very Short-Term Load Forecasting (VSTLF) model for the interim electricity market of ISO New England (ISO-NE). The main outcome of the forecaster is the 5-minute forecast of New England internal system demand that will be used directly by the 5-min real-time resource dispatch function in the existing spot market. The design of the ANN structure, the selection of the training sets, raw data pre-processing, the training process itself as well as validation and testing will be discussed in details. The ANN model has been tested under a wide variety of conditions and the results of the study demonstrate a high forecast accuracy. An off-line training system based on back-propagation algorithm is developed to support the training and retraining of the ANN of the VSTLF model. A real-time VSTLF application is developed and integrated into ISO-NE's Energy Management System (EMS).

引用

页码：217 / 222

页数：6

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