Short-term load forecasting with local ANN predictors
- Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States). Center for Energy and the Global Environment
A new technique for artificial neural network (ANN) based short-term load forecasting (STLF) is present in this paper. The technique implemented active selection of training data, employing the k-nearest neighbors concept. A novel concept of pilot simulation was used to determine the number of hidden units for the ANNs. The ensemble of local ANN predictors was used to produce the final forecast, whereby the iterative forecasting procedure used a simple average of ensemble ANNs. Results obtained using data from two US utilities showed forecasting accuracy comparable to those using similar techniques. Excellent forecasts for one-hour-ahead and five-days-ahead forecasting, robust behavior for sudden and large weather changes, low maximum errors and accurate peak-load predictions are some of the findings discussed in the paper.
- OSTI ID:
- 678005
- Journal Information:
- IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 3 Vol. 14; ISSN 0885-8950; ISSN ITPSEG
- Country of Publication:
- United States
- Language:
- English
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