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Machine learning

Artificial Neural Networks for Midterm Electrical Load Forecasting

Strategic Insights

— 1 min read

Artificial neural networks have emerged as a promising solution for predicting midterm electrical load demands. According to a recent study from Sulaimani Polytechnic University, researchers utilized a medium-term load forecasting model based on artificial neural networks (ANNs) to predict the electrical demand for Duhok city up to the year 2025. The study found that ANNs are effective in forecasting midterm electrical load demands due to their simplicity, easy implementation, and superior performance. This research has significant implications for reducing operational costs and emissions in power plants.

Key Takeaways:

  • The demand for electricity in Duhok city is increasing, requiring efforts to provide the required load.
  • Building new electricity generation units and/or importing electricity from neighboring countries are strategies to fulfill these needs.
  • Midterm electrical load forecasting is essential for estimating the required power and planning maintenance, expansion, and improving system performance of electrical power generation.
  • ANN-based forecasting has been shown to be effective in predicting midterm electrical load demands.
  • The study used available data for the power system of Duhok city to predict the load demand up to the year 2025.
  • ANN-based forecasting can reduce operational costs and emissions in power plants by reducing fuel consumption.
  • Keywords for this study include: Sulaimani Polytechnic University, Machine Learning, Emerging Technologies, Artificial Neural Networks.

Statistics:

  • 2025: The year up to which the researchers predicted the electrical demand for Duhok city.
  • 11(2): The volume and issue number of the Sulaimani Journal for Engineering Sciences in which the study was published.
  • 31-43: The page numbers of the study in the Sulaimani Journal for Engineering Sciences.
  • 2025: The year in which the study concluded that ANN-based forecasting is useful for estimating the required power to be extracted from power systems.
  • 2155: The page number of the Journal of Engineering that published the news report on the study.

Sources:

  • Sulaimani Journal for Engineering Sciences, 2025,11(2):31-43.
  • University of Sulaimania, publisher.
  • Average Midterm Electrical Load Forecasting for Duhok City Based on Artificial Neural Network.
  • NewsRx LLC, 2025,
  • Journal of Engineering, August 18, 2025; p 2155.

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