Title :
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
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;
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
DOI :
10.1109/PICA.2001.932351
