Artificial neural network-based distribution substation and feeder load forecast

Yasuoka, J.,Brittes, J.L.P.,Schmidt, H.P., Jardini, J.A.

IEE Conf. Publ No. 482)（2001）

Cited 11|Views0

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Abstract

A methodology for estimating future demand values at both distribution substation and primary feeder levels is described in this paper. The software implementation of the proposed methodology is already running in a 138/11.9-kV, 3×40-MVA distribution substation. Results obtained with this implementation are very encouraging, even when using as little historical data as 3 months. Forecast error is also very low when a demand curve substantially different from the ones presented to the artificial neural network in its training phase are used in the processing mode. A separate module for dealing with load transfers between primary feeders during contingencies is currently in its final stages of development

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Key words

distribution networks,learning (artificial intelligence),load forecasting,multilayer perceptrons,power system analysis computing,transformer substations,11.9 kV,138 kV,40 MVA,artificial neural network,computer simulation,demand curve,distribution feeder,distribution substation,load forecast,load transfer,primary feeder levels,software implementation,training phase,

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