DAILY NIGERIAN PEAK LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORK WITH SEASONAL INDICES
DOI:
https://doi.org/10.4314/njt.331.756Keywords:
load forecasting, neural network, seasonal indices, back propagation, actual loadAbstract
A daily peak load forecasting technique that uses artificial neural network with seasonal indices is presented in this paper. A neural network of relatively smaller size than the main prediction network is used to predict the daily peak load for a period of one year over which the actual daily load data are available using one step ahead prediction. Daily seasonal indices are calculated as a ratio of the predicted load to the actual load. The ith index is used as an additional input to a main network that predicts the load for the ith day of the year following the one for which the indices were computed. Both neural networks are trained by the back propagation algorithm. The technique is illustrated with data derived from the Nigerian national electric power system. Results obtained are good enough to meet the requirements of practical systems and show appreciable improvement over the normal one step ahead prediction with neural network.
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