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Updated: Jun 29, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Predictive models for short-term load forecasting in the UK's electrical grid.

Yusuf A Sha'aban1

  • 1Department of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin, Kingdom of Saudi Arabia.

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Summary

Accurate short-term load forecasting is crucial for managing increased electricity demand from electric vehicles (EVs). Machine learning models, specifically Support Vector Regression (SVR) and Artificial Neural Networks (ANN), show high precision for predicting grid loads.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Energy Systems

Background:

  • Global deployment of electric vehicles (EVs) is accelerating the energy transition.
  • Increased EV adoption raises concerns about potential strain on electricity grids due to higher demand.
  • Accurate short-term load forecasting is essential for efficient grid planning, operation, and control.

Purpose of the Study:

  • To develop and evaluate robust machine learning models for short-term load forecasting in the UK's power system.
  • To compare the performance of Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) for load prediction.
  • To assess the accuracy of forecasting models for both half-hourly and hourly electricity demand.

Main Methods:

  • Utilized net imports data from the UK's power system spanning 2010-2020.
  • Applied machine learning techniques including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR).
  • Evaluated model performance using metrics such as Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and Correlation of Determination (R2).

Main Results:

  • Support Vector Regression (SVR) demonstrated the highest accuracy for half-hourly load forecasts, achieving an R-value of 99.85%.
  • Artificial Neural Networks (ANN) provided the best performance for hourly load forecasts, with an R-value of 99.71%.
  • Gaussian Process Regression (GPR) also showed strong predictive capabilities for both forecast horizons.

Conclusions:

  • Machine learning methods, including SVR, ANN, and GPR, are highly reliable and precise for short-term load forecasting.
  • The SVR model is particularly effective for half-hourly predictions, while the ANN model excels in hourly predictions.
  • Accurate load forecasting is vital for integrating renewable energy and managing the evolving demands of e-mobility.