An adaptive neural network approach to one-week ahead load forecasting

T. M. Peng, N. F. Hubele, G. G. Karady

Research output: Contribution to journalArticlepeer-review

99 Scopus citations


A new neural network approach is applied to one-week ahead load forecasting. This approach uses a linear adaptive neuron or adaptive linear combiner called “Adaline.” An energy spectrum is used to analyze the periodic components in a load sequence. The load sequence mainly consists of three components: base load component, and low and high frequency load components. Each load component has a unique frequency range. Load decomposition is made for the load sequence using digital filters with different passband frequencies After load decomposition, each load component can be forecasted by an Adaline. Each Adaline has an input sequence, an output sequence, and a desired response-signal sequence. It also has a set of adjustable parameters called the weight vector. In load forecasting, the weight vector is designed to make the output sequence, the forecasted load, follow the actual load sequence; it also has a minimized Least Mean Square error. This approach is useful in forecasting unit scheduling commitments. Mean absolute percentage errors of less than 3.4 percent are derived from five months of utility data, thus demonstrating the high degree of accuracy that can be obtained without dependence on weather forecasts.

Original languageEnglish (US)
Pages (from-to)1195-1203
Number of pages9
JournalIEEE Transactions on Power Systems
Issue number3
StatePublished - Aug 1993


  • Cadaline
  • adaptive signal processing
  • analysis
  • load decomposition
  • neural network
  • spectrum

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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