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An artificial neural network hourly temperature forecaster with applications in load forecasting

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4 Author(s)
Khotanzad, A. ; Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA ; Davis, M.H. ; Abaye, A. ; Maratukulam, D.J.

Many power system short-term load forecasting techniques use forecast hourly temperatures in generating a load forecast. Some utility companies, however, do not have access to a weather service that provides these forecasts. To fill this need, a temperature forecaster, based on artificial neural networks, has been developed that predicts hourly temperatures up to seven days ahead. The prediction is based on forecast daily high and low temperatures and other information that would be readily available to any electric utility. The forecaster has been evaluated using data from eight utilities in the USA. The mean absolute error of one day ahead forecasts for these utilities is 1.48°F. The forecaster is implemented at several electric utilities and is being used in production environments

Published in:

Power Systems, IEEE Transactions on  (Volume:11 ,  Issue: 2 )