Control
Wafeeq Sh. Hanna; Velar H. Elias; Dlawar R. Maruf
Abstract
The load forecasting is a human or computational technique foraccurate preanticipation of electrical load to enhance reliable operation andoptimal planning control of system plant for electrical energy flowing withoutfacing any economical and technical limitations, therefore appropriateestimation for ...
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The load forecasting is a human or computational technique foraccurate preanticipation of electrical load to enhance reliable operation andoptimal planning control of system plant for electrical energy flowing withoutfacing any economical and technical limitations, therefore appropriateestimation for present and future consumption cost of electrical loads which arenecessary to predict the load demand for generating near to accurate power.During advanced technology at the last few decades, artificial neuralnetworks(ANNs) have been extensively employed in electrical system, they aretrained using historical data obtained from plant station. This work is intendedto be a study of short-term load forecasting (STLF) basis for a power predictedapplied to the actual past load data displayed from Azadi station for Feb.2022were used in training and validation system of neural grid. The result wasevaluated by mean square percentage error of (32.7) for the forecastingdynamic time series method to solve the data over hours, days, and weeks inadvance, using a kind of non-linear filtering. Short-term load forecasting triedout with main stages; predicted power load data sets, network training, andforecasting. Neural network used has 3-layers: an input, a hidden, and anoutput layer. The number of hidden layer neurons can be varied for the differentnetwork performance. The active power generation faces economical andtechnical challenges, therefore appropriate evaluation of loads are muchneeded
