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<p>Forecasting green energy with high accuracy is essential for effective energy management and ensuring the reliability of power grids. This is particularly important given the fluctuating nature of renewable sources such as solar and wind power, which require careful regulation to provide an uninterrupted electricity supply. To improve forecasting accuracy, this study integrates cutting-edge Dynamic Temporal Convolutional Networks (DTCNs), practical feature selection methods, and optimization through a novel metaheuristic algorithm. In the initial evaluation, our model achieved a Mean Squared Error (MSE) of 0.0845 and an R-squared (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation>) score of 0.7265. These results indicate that the model was able to learn and replicate some of the underlying patterns in the training data. To enhance model performance, a feature selection process was applied to eliminate irrelevant information. This refinement reduced the MSE from 0.6988 to 0.0022 and improved the <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> score to 0.9005, thereby clarifying the relationship between input variables and green energy generation. Further performance improvements were obtained using a metaheuristic optimizer, the iHow Optimization Algorithm (iHOW). This approach led to a significant reduction in MSE to <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(1.14 \times 10^{-5}\)</EquationSource> </InlineEquation> and an increase in <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> to 0.9804. The combined use of feature selection, advanced optimization, and deep learning contributes substantially to forecasting precision. This approach offers a practical tool for energy professionals, supporting more reliable and efficient renewable energy production and contributing to a sustainable future.</p>

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Explainable AI for intelligent green energy forecasting: deep learning with iHow optimization algorithm (iHOW)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Mahmoud Shabrawy,
  • Khaled Sh. Gaber,
  • Marwa M. Eid,
  • Amel Ali Alhussan,
  • Doaa Sami Khafaga,
  • El-Sayed M. El-kenawy

摘要

Forecasting green energy with high accuracy is essential for effective energy management and ensuring the reliability of power grids. This is particularly important given the fluctuating nature of renewable sources such as solar and wind power, which require careful regulation to provide an uninterrupted electricity supply. To improve forecasting accuracy, this study integrates cutting-edge Dynamic Temporal Convolutional Networks (DTCNs), practical feature selection methods, and optimization through a novel metaheuristic algorithm. In the initial evaluation, our model achieved a Mean Squared Error (MSE) of 0.0845 and an R-squared ( \(R^2\) ) score of 0.7265. These results indicate that the model was able to learn and replicate some of the underlying patterns in the training data. To enhance model performance, a feature selection process was applied to eliminate irrelevant information. This refinement reduced the MSE from 0.6988 to 0.0022 and improved the \(R^2\) score to 0.9005, thereby clarifying the relationship between input variables and green energy generation. Further performance improvements were obtained using a metaheuristic optimizer, the iHow Optimization Algorithm (iHOW). This approach led to a significant reduction in MSE to \(1.14 \times 10^{-5}\) and an increase in \(R^2\) to 0.9804. The combined use of feature selection, advanced optimization, and deep learning contributes substantially to forecasting precision. This approach offers a practical tool for energy professionals, supporting more reliable and efficient renewable energy production and contributing to a sustainable future.