Electrical Load Forecasting

  • Yuan-Yih Hsu
  • Chien-Chun Yang
Chapter

Abstract

Application of artificial neural networks (ANNs) to forecast the hourly loads of an electrical power system is examined in this chapter. Two types of ANN’s, i.e., the Kohonen’s self-organising feature maps and the feedforward multilayer neural networks, are employed for load forecasting. Kohonen’s self-organising feature map, which is a kind of ANN with unsupervised learning scheme, is first used to identify those days with similar hourly load patterns. These days with similar load patterns are said to be of the same day type. The load pattern of the day under study is obtained by averaging the load patterns of several days in the past which are of the same day type as the given day. After the hourly load pattern has been reached, a multilayer feedforward neural network is designed to predict daily peak load and valley load. Once the peak load and valley load and the hourly load pattern are available, the desired hourly loads can be readily computed. The effectiveness of the proposed neural network is demonstrated by the short-term load forecasting of Taiwan power system.

Keywords

Peak Load Output Node Input Pattern Load Pattern Load Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Yuan-Yih Hsu
    • 1
  • Chien-Chun Yang
    • 1
  1. 1.Department of Electrical EngineeringNational Taiwan UniversityTaipeiTaiwan

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