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In smart cities, the demand forecasting problem is inherently dynamic and difficult to predict. A novel forecasting framework, EleKAN is proposed, that utilizes the Temporal Kolmogorov-Arnold Networks (TKANs), which are designed to leverage enhanced efficiency, accuracy, and reslience in the multistep time series predictions. TKANs integrate the interpretability and performance strengths of Kolmogorov-Arnold Networks (KANs) with the temporal dependency management capabilities of recurrent architectures, effectively addressing the limitations of existing models. The proposed framework is applied to a benchmarking Australian New South Wales (NSW) electricity market, which includes various features including total demand, demand delay, power consumption, and temporal factors such as weekdays, holidays, and time intervals. Experimental results demonstrate that EleKAN achieves a Root Mean Square Error (RMSE) of 0.0030, an R-squared ( \(R^2\) ) score of 0.9757, significantly improving the precision and dependability of power demand estimates. A percentage improvement of 10. 9%, 6. 1%, and 9. 7% is obtained in the score of \(R^{2}\) over LSTM and GRU respectively. This makes it particularly valuable for long-term energy management and planning in smart cities.

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EleKAN: Temporal Kolmogorov-Arnold Networks for Price and Demand Forecasting Framework in Smart Cities

  • 会议论文

  • 发表日期：

    2025年

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  • Pronaya Bhattacharya,
  • Tamoghna Mukherjee

摘要

In smart cities, the demand forecasting problem is inherently dynamic and difficult to predict. A novel forecasting framework, EleKAN is proposed, that utilizes the Temporal Kolmogorov-Arnold Networks (TKANs), which are designed to leverage enhanced efficiency, accuracy, and reslience in the multistep time series predictions. TKANs integrate the interpretability and performance strengths of Kolmogorov-Arnold Networks (KANs) with the temporal dependency management capabilities of recurrent architectures, effectively addressing the limitations of existing models. The proposed framework is applied to a benchmarking Australian New South Wales (NSW) electricity market, which includes various features including total demand, demand delay, power consumption, and temporal factors such as weekdays, holidays, and time intervals. Experimental results demonstrate that EleKAN achieves a Root Mean Square Error (RMSE) of 0.0030, an R-squared ( \(R^2\) ) score of 0.9757, significantly improving the precision and dependability of power demand estimates. A percentage improvement of 10. 9%, 6. 1%, and 9. 7% is obtained in the score of \(R^{2}\) over LSTM and GRU respectively. This makes it particularly valuable for long-term energy management and planning in smart cities.