Document Type : Research Paper
Authors
1 Electrical Department, College of Engineering, Salahaddin University, Erbil, Iraq
2 3Electrical Department, College of Engineering, Salahaddin University, Erbil, Iraq
Abstract
The load forecasting is a human or computational technique for
accurate preanticipation of electrical load to enhance reliable operation and
optimal planning control of system plant for electrical energy flowing without
facing any economical and technical limitations, therefore appropriate
estimation for present and future consumption cost of electrical loads which are
necessary to predict the load demand for generating near to accurate power.
During advanced technology at the last few decades, artificial neural
networks(ANNs) have been extensively employed in electrical system, they are
trained using historical data obtained from plant station. This work is intended
to be a study of short-term load forecasting (STLF) basis for a power predicted
applied to the actual past load data displayed from Azadi station for Feb.2022
were used in training and validation system of neural grid. The result was
evaluated by mean square percentage error of (32.7) for the forecasting
dynamic time series method to solve the data over hours, days, and weeks in
advance, using a kind of non-linear filtering. Short-term load forecasting tried
out with main stages; predicted power load data sets, network training, and
forecasting. Neural network used has 3-layers: an input, a hidden, and an
output layer. The number of hidden layer neurons can be varied for the different
network performance. The active power generation faces economical and
technical challenges, therefore appropriate evaluation of loads are much
needed
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