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Short-Term Load Forecasting Using Nonlinear Dynamic Time Series Autoregressive with Feedback for AZADI Control-Station Erbil-Iraq | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Volume 23, Issue 4, December 2023, Pages 24-34 PDF (983.93 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.23.4.3 | ||
| Authors | ||
| Wafeeq Sh. Hanna1; Velar H. Elias* 1; Dlawar R. Maruf2 | ||
| 1Electrical Department, College of Engineering, Salahaddin University, Erbil, Iraq | ||
| 23Electrical 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 | ||
| Keywords | ||
| ANN; STLF; MW; Neurons | ||
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