An implementation of a neural network based load forecasting model for the EMS
Abstract
This paper presents the development and implementation of an Artificial Neural Network (ANN) based short-term system load forecasting model for the Energy Control Center of the Pacific Gas and Electric Company (PG and E). Insights gained during the development of the model regarding the choice of the input variables and their transformations, the design of the ANN structure, the selection of the training cases and the training process itself will be described in the paper. Attention was paid to model accurately special events, such as holidays, heat-waves, cold snaps and other conditions that disturb the normal pattern of the load.The significant impact of special events on the model's performance was established through testing of an existing load forecasting package that is currently in production use. The new model has been tested under a wide variety of conditions and it is shown in this paper to produce excellent results. Comparison results between the existing, regression based model and the ANN model are very encouraging. The ANN model consistently outperforms the existing model in terms of both, average errors over a long period of time and number of large errors. The ANN model has also been integrated with PG and E's Energymore »
- Authors:
-
- Pacific Gas and Electric Co., San Francisco, CA (United States)
- Peng (Tie-Mao), San Francisco, CA (United States)
- Publication Date:
- OSTI Identifier:
- 6614383
- Resource Type:
- Journal Article
- Journal Name:
- IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States)
- Additional Journal Information:
- Journal Volume: 9:4; Journal ID: ISSN 0885-8950
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 24 POWER TRANSMISSION AND DISTRIBUTION; POWER DEMAND; FORECASTING; POWER SYSTEMS; COMPUTERIZED CONTROL SYSTEMS; NEURAL NETWORKS; TRAINING; CONTROL SYSTEMS; DEMAND; EDUCATION; ENERGY SYSTEMS; ON-LINE CONTROL SYSTEMS; ON-LINE SYSTEMS; 240100* - Power Systems- (1990-)
Citation Formats
Papalexopoulos, A D, Hao, S, and Peng, T M. An implementation of a neural network based load forecasting model for the EMS. United States: N. p., 1994.
Web. doi:10.1109/59.331456.
Papalexopoulos, A D, Hao, S, & Peng, T M. An implementation of a neural network based load forecasting model for the EMS. United States. https://doi.org/10.1109/59.331456
Papalexopoulos, A D, Hao, S, and Peng, T M. 1994.
"An implementation of a neural network based load forecasting model for the EMS". United States. https://doi.org/10.1109/59.331456.
@article{osti_6614383,
title = {An implementation of a neural network based load forecasting model for the EMS},
author = {Papalexopoulos, A D and Hao, S and Peng, T M},
abstractNote = {This paper presents the development and implementation of an Artificial Neural Network (ANN) based short-term system load forecasting model for the Energy Control Center of the Pacific Gas and Electric Company (PG and E). Insights gained during the development of the model regarding the choice of the input variables and their transformations, the design of the ANN structure, the selection of the training cases and the training process itself will be described in the paper. Attention was paid to model accurately special events, such as holidays, heat-waves, cold snaps and other conditions that disturb the normal pattern of the load.The significant impact of special events on the model's performance was established through testing of an existing load forecasting package that is currently in production use. The new model has been tested under a wide variety of conditions and it is shown in this paper to produce excellent results. Comparison results between the existing, regression based model and the ANN model are very encouraging. The ANN model consistently outperforms the existing model in terms of both, average errors over a long period of time and number of large errors. The ANN model has also been integrated with PG and E's Energy Management System (EMS). It is envisioned that the ANN model will eventually substitute the existing model to support the Company's real-time operations. In the interim both models will be available for production use.},
doi = {10.1109/59.331456},
url = {https://www.osti.gov/biblio/6614383},
journal = {IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States)},
issn = {0885-8950},
number = ,
volume = 9:4,
place = {United States},
year = {Tue Nov 01 00:00:00 EST 1994},
month = {Tue Nov 01 00:00:00 EST 1994}
}