Effect of probabilistic inputs on neural network-based electric load forecasting

Damitha K. Ranaweera, George G. Karady, R. G. Farmer

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

This letter presents a novel method to include the uncertainties of the weather-related input variables in neural network-based electric load forecasting models. The new method consists of traditionally trained neural networks and a set of equations to calculate the mean value and confidence intervals of the forecasted load. This method was tested for daily peak load forecasts for one year by using modified data from a large power system. The tests indicate that in addition to the confidence interval, the new method provides a more accurate mean forecast than a multilayer perceptron networks alone.

Original languageEnglish (US)
Pages (from-to)1528-1532
Number of pages5
JournalIEEE Transactions on Neural Networks
Volume7
Issue number6
DOIs
StatePublished - 1996

Fingerprint

Electric load forecasting
Load Forecasting
Neural Networks
Neural networks
Multilayer neural networks
Confidence interval
Forecast
Perceptron
Weather
Mean Value
Power System
Multilayer
Uncertainty
Calculate

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture

Cite this

Effect of probabilistic inputs on neural network-based electric load forecasting. / Ranaweera, Damitha K.; Karady, George G.; Farmer, R. G.

In: IEEE Transactions on Neural Networks, Vol. 7, No. 6, 1996, p. 1528-1532.

Research output: Contribution to journalArticle

Ranaweera, Damitha K. ; Karady, George G. ; Farmer, R. G. / Effect of probabilistic inputs on neural network-based electric load forecasting. In: IEEE Transactions on Neural Networks. 1996 ; Vol. 7, No. 6. pp. 1528-1532.
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