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Effect of temperature on short term load forecasting using an integrated ANN
Shanti K. Swarup, , Swarup K.S, , Rao A.H.
Published in
2004
Volume: 72
   
Issue: 1
Pages: 95 - 101
Abstract
An integrated Artificial Neural Network (ANN) approach to Short-Term Load Forecasting (STLF) is proposed in this paper. Four modules consisting of the Basic ANN, Peak and Valley ANN, Averager and Forecaster and Adaptive Combiner form the integrated method for load forecasting. The Basic ANN uses the historical data of load and temperature to predict the next 24h load, while the Peak and Valley ANN uses the past peak and valley data of load and temperatures, respectively. The Averager captures the average variation of the load from the previous load behaviour, while the adaptive combiner uses the weighted combination of outputs from the Basic ANN and the Forecaster, to forecast the final load. The regression based and time series methods are conceptually incorporated into the ANN to obtain an integrated load forecasting approach. © 2004 Elsevier B.V. All rights reserved.
About the journal
JournalElectric Power Systems Research
ISSN03787796
Open AccessNo
Concepts (11)
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    Back propagation algorithm
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    Expansion planning
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    INPUT-OUTPUT RELATION
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    Short-term load forecasting
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    Algorithms
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    Computer simulation
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    Electric loads
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    Integration
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    Neural networks
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    Parameter estimation
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    Electric power systems