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Load forecasting analysis for the improved collaborative optimization algorithm of ground source heat pump air conditioning. 

Auteurs : LIU Z., CAO M.

Type d'article : Article de la RIF

Résumé

To address the challenges of model hyperparameter tuning and multi-parameter coupling dynamic modeling in ground source heat pump load forecasting, this paper innovatively proposes a predictive framework that combines hybrid optimization algorithms with deep learning. This model uses an improved quantum genetic algorithm combined with an enhanced adaptive differential evolution algorithm to adjust dynamic parameters, achieving intelligent optimization of key hyperparameters for the BiLSTM model. Additionally, an improved BiLSTM network is constructed, incorporating residual connections and a multi-head attention mechanism to more accurately capture the delayed response characteristics caused by thermal inertia. Compared to existing models, this model outperforms others in load forecasting, providing technical support for the intelligent control and energy efficiency optimization of ground source heat pump air conditioning.

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Format PDF

Pages : 13

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    20 €

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Détails

  • Titre original : Load forecasting analysis for the improved collaborative optimization algorithm of ground source heat pump air conditioning. 
  • Identifiant de la fiche : 30034941
  • Langues : Anglais
  • Sujet : Technologie
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 187
  • Date d'édition : 07/2026
  • DOI : http://dx.doi.org/https://doi.org/10.1016/j.ijrefrig.2026.106934

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