A neural network based very short term load forecaster for the interim ISO New England electricity market system

Author

Shamsollahi, Payman ; Cheung, Kwok W. ; Chen, Quan ; Germain, Edward H.

Author_Institution

ALSTOM ESCA Corp., Bellevue, WA, USA

fYear

2001

fDate

2001

Firstpage

217

Lastpage

222

Abstract

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 are discussed in detail. 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)

Keywords

electricity supply industry; learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; 5 min; ISO New England; artificial neural network; back-propagation algorithm; electricity market system; energy management system; five-minute forecast; five-minute real-time resource dispatch function; high forecast accuracy; interim electricity market; off-line training system; raw data pre-processing; spot market; training process; training sets; very short-term load forecasting; Artificial neural networks; Economic forecasting; Electricity supply industry; Load forecasting; Load modeling; Management training; Neural networks; Predictive models; Real time systems; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Industry Computer Applications, 2001. PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society International Conference on

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-6681-6

Type

conf

DOI

10.1109/PICA.2001.932351

Filename

932351