A real-time implementation of short-term load forecasting for distribution power systems

This paper presents a practical real-time implementation of weather adaptive short-term load forecasting for distribution power utilities. The implementation is accomplished by utilizing a comprehensive load forecasting model consisting of time series, nonlinear load-weather functions and a residual...

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Veröffentlicht in:IEEE transactions on power systems Jg. 9; H. 2; S. 988 - 994
Hauptverfasser: Fan, J.Y., McDonald, J.D.
Format: Journal Article Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.05.1994
Institute of Electrical and Electronics Engineers
Schlagworte:
240100 - Power Systems- (1990-)
292000 - Energy Planning & Policy- Supply, Demand & Forecasting
296000 - Energy Planning & Policy- Electric Power
Applied sciences
COMPUTER CODES
DEMAND
Electrical engineering. Electrical power engineering
Electrical power engineering
ENERGY PLANNING, POLICY AND ECONOMY
Exact sciences and technology
FORECASTING
Least squares approximation
Load forecasting
Load modeling
Numerical models
Operation. Load control. Reliability
Parameter estimation
POWER DEMAND
POWER DISTRIBUTION SYSTEMS
Power networks and lines
POWER TRANSMISSION AND DISTRIBUTION
Predictive models
REAL TIME SYSTEMS
Recursive estimation
S CODES
Software packages
Testing
Weather forecasting
Short term
Atmospheric condition
Software tool
Model following
Electrical network
Demand forecasting
Development
Adaptive estimation
Real time
Modeling
Distribution network
ISSN:0885-8950, 1558-0679
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Zusammenfassung:This paper presents a practical real-time implementation of weather adaptive short-term load forecasting for distribution power utilities. The implementation is accomplished by utilizing a comprehensive load forecasting model consisting of time series, nonlinear load-weather functions and a residual load function represented by an ARMA (auto-regressive moving average) model. The model parameters are estimated and updated online using the WRLS (weighted recursive least squares) algorithm. A variable forgetting factor (VFF) technique is incorporated in the WRLS algorithm for improved model tracking and numerical performance in real-time operation. A software package, STLF, is developed with the proposed implementation method for distribution power utilities. Practical operation of the STLF in several power utilities has demonstrated great success. Offline testing and online operation has consistently shown satisfactory performance with the mean absolute error (MAE) mostly less than 2% for a less than 24-hour ahead forecast and less than 2.5% for a less than 168-hour ahead forecast.< >
Bibliographie:ObjectType-Article-2
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ISSN:0885-8950
1558-0679
DOI:10.1109/59.317646