A specification of neural network applications in the load forecasting problem

The effectiveness of an artificial neural network (ANN) approach to short-term load forecasting in power systems is investigated. Examples demonstrate the learning ability of a neural net in predicting the peak load of the day by using different preprocessing approaches and by exploiting different i...

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Bibliographic Details
Main Authors: Azzam-ul-Asar, J.R., McDonald, J.R., Khan, M.I.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Backpropagation algorithms
Data preprocessing
Expert systems
Load forecasting
Neural networks
Power system control
Power system planning
Power systems
Temperature
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Summary:The effectiveness of an artificial neural network (ANN) approach to short-term load forecasting in power systems is investigated. Examples demonstrate the learning ability of a neural net in predicting the peak load of the day by using different preprocessing approaches and by exploiting different input patterns to observe the possible correlation between historical load and temperatures. A number of ANNs have been demonstrated with emphasis given to their practical implementation for power system control and planning purposes. The network is trained on actual power utility load data using a backpropagation algorithm. The prospects for applying a combined solution using artificial neural networks and expert systems, called the expert network, is also discussed. It may give a more complete solution to the forecasting problem than either system alone can provide.< >
DOI:10.1109/CCA.1992.269810