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ANNSTLF-a neural-network-based electric load forecasting system.

A Khotanzad1, R Afkhami-Rohani, T L Lu

  • 1Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
Summary
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Accurate short-term load forecasting is vital for electric utilities. The Artificial Neural-Network Short-Term Load Forecaster (ANNSTLF) system enhances prediction accuracy by considering weather, improving operational planning and economic impact.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Short-term load forecasting is crucial for electric utility operations and planning.
  • Forecast accuracy directly impacts a utility's economic performance.
  • Existing methods may not fully capture complex load variations.

Purpose of the Study:

  • To describe the Artificial Neural-Network Short-Term Load Forecaster (ANNSTLF) system.
  • To detail its architecture, including load and weather forecasting components.
  • To evaluate the performance and adaptability of the ANNSTLF system.

Main Methods:

  • Utilizes a multiple Artificial Neural Network (ANN) strategy for trend capture.
  • Employs a multilayer perceptron trained with the error backpropagation learning rule.

Related Experiment Videos

  • Incorporates an adaptive scheme for online weight adjustment and site-independent models.

    • Main Results:

    • ANNSTLF is widely adopted, used by 32 utilities in the USA and Canada.
    • The system effectively forecasts hourly loads, temperature, and relative humidity.
    • Models demonstrate site independence, requiring only hidden layer node adjustment for new data.

    Conclusions:

    • ANNSTLF provides an effective and adaptable solution for short-term load forecasting.
    • The system's ability to integrate weather forecasting enhances load prediction accuracy.
    • ANNSTLF represents a significant advancement in utility load forecasting technology.