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<p>This study explores next-day electricity demand forecasting using the Kalman filter for parameter learning in a Radial Basis Function (RBF) NARMAX model. This approach is compared to batch regression-based RBF NARMAX models, Feedforward Neural Networks (FFNN), and Recurrent Neural Networks (RNN), incorporating temperature as an exogenous variable. The models are trained using a novel combination of step-forward validation and grid search for parameter selection, which enhances computational efficiency, accuracy, and model complexity. Mean Squared Error (MSE) is employed to determine the optimal parameters. Results indicate that the Kalman filter-based RBF model achieves the highest accuracy with the given dataset, outperforming neural networks in predictive performance. It proves computationally efficient and effectively captures seasonal patterns in the time series. Model performance is evaluated using MAPE, MSE, MAE, and <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="40435_2025_1638_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\({R}^2\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi>R</mi> </mrow> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation>, achieving over 90% accuracy on both the training and testing sets for the recommended models.</p>

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A novel approach to electricity demand forecasting: an optimized Kalman filter-based RBF model

  • 期刊论文

  • 发表日期：

    2025年4月19日

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  • Agresa Qosja,
  • Didier Georges,
  • Eralda Gjika,
  • Ligor Nikolla,
  • Arben Cela

摘要

This study explores next-day electricity demand forecasting using the Kalman filter for parameter learning in a Radial Basis Function (RBF) NARMAX model. This approach is compared to batch regression-based RBF NARMAX models, Feedforward Neural Networks (FFNN), and Recurrent Neural Networks (RNN), incorporating temperature as an exogenous variable. The models are trained using a novel combination of step-forward validation and grid search for parameter selection, which enhances computational efficiency, accuracy, and model complexity. Mean Squared Error (MSE) is employed to determine the optimal parameters. Results indicate that the Kalman filter-based RBF model achieves the highest accuracy with the given dataset, outperforming neural networks in predictive performance. It proves computationally efficient and effectively captures seasonal patterns in the time series. Model performance is evaluated using MAPE, MSE, MAE, and \({R}^2\) R 2 , achieving over 90% accuracy on both the training and testing sets for the recommended models.