Multistage Artificial Neural Network Short-Term Load Forecasting Engine With Front-End Weather Forecast

A significant portion of the operating cost of utilities comes from energy production. To minimize the cost, unit commitment (UC) scheduling can be used to determine the optimal commitment schedule of generation units to accommodate the forecasted demand. The load forecast is a prerequisite for UC p...

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Bibliographic Details
Published in:IEEE transactions on industry applications 2007-11, Vol.43 (6), p.1410-1416
Main Authors: Methaprayoon, K., Wei-Jen Lee, Rasmiddatta, S., Liao, J.R., Ross, R.J.
Format: Article
Language:English
Subjects:
Artificial neural networks
Cost function
Demand
Demand forecasting
Dispatching
Engines
Error analysis
Error detection
Forecasting
Load forecasting
Multistage
Neural network
Neural networks
Optimization
Performance analysis
Production
Resource management
short-term load forecasting
Studies
unit commitment (UC) scheduling
Utilities
weather forecast
Weather forecasting
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Summary:A significant portion of the operating cost of utilities comes from energy production. To minimize the cost, unit commitment (UC) scheduling can be used to determine the optimal commitment schedule of generation units to accommodate the forecasted demand. The load forecast is a prerequisite for UC planning. The projected load of up to seven days is important for the allocation of generation resources. Hour-ahead forecast is used for optimally dispatching online resources to supply the next hour load. This paper addresses the systematic design of a multistage artificial-neural-network-based short-term load forecaster (ANNSTLF). The developed ANNSTLF engine has been utilized in a real utility system. The performance analysis over the past year shows that a majority of the forecast error was detected in a consistent period with a large temperature forecast error. The enhancement of ANNSTLF is proposed to improve the forecasting performance. The comparison of forecasting accuracy due to this enhancement is analyzed.
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2007.908190