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The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks

Author

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  • Yang, Yiran
  • Li, Gang
  • Luo, Tao
  • Al-Bahrani, Mohammed
  • Al-Ammar, Essam A.
  • Sillanpaa, Mika
  • Ali, Shafaqat
  • Leng, Xiujuan

Abstract

The heating and Cooling loads are the main contributors to energy consumption in buildings, and predicting them can prevent many potential financial losses in civil engineering projects. Using the benefits of the neural networks, including support vector machine, gated recurrent unit, extreme learning machine, long short-term memory, and shuffled frog leaping algorithm as an optimizer, the present study aims to predict the energy consumption of the building. The empirical data are trained using the selected networks and optimized through a shuffled frog-leaping algorithm. Also, the statistical criteria are analyzed to specify the best network in terms of accuracy and speed. The obtained results and the convergence rate represent the remarkable capability of the shuffled frog leaping algorithm for optimization. According to the statistical results, long short-term memory and support vector machine are introduced as the best neural network for cooling and heating load forecast, respectively. According to the obtained results, for the cooling load prediction, LSTM-SFLA presents the best performance by an R2 of 0.9761. On the other hand, for the heating load prediction, SVR-SFLA has the optimal performance with an R2 of 0.9583. The results indicate that using the SFLA optimizer could assist in improving the prediction performance.

Suggested Citation

  • Yang, Yiran & Li, Gang & Luo, Tao & Al-Bahrani, Mohammed & Al-Ammar, Essam A. & Sillanpaa, Mika & Ali, Shafaqat & Leng, Xiujuan, 2023. "The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks," Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:energy:v:268:y:2023:i:c:s0360544222034351
    DOI: 10.1016/j.energy.2022.126548
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