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Communication Dans Un Congrès Année : 2024

Hyperparameter optimization of artificial neural network in building energy simulation

Résumé

Artificial Neural Networks (ANNs) play a significant role in emulating Building Energy Simulation (BES), forecasting building energy consumption, and optimizing energy retrofit measures. The determination of the appropriate ANN's architecture is a complex issue. Hyperparameter optimization (HPO) involves aligning ANN prediction results with data to achieve optimal performance by tuning the ANN's hyperparameters. This method is applied to retrofit an existing low-energy building. To construct the ANN for retrofit measures, data obtained through Latin Hypercube sampling are utilized. The ANN is used to predict the hourly energy consumption, the hourly energy generation, and thermal comfort in the retrofit scenario. The results underscore the importance of HPO in achieving predictions with an ANN, demonstrating an R² error exceeding 0.9.
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Dates et versions

hal-04680354 , version 1 (28-08-2024)

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  • HAL Id : hal-04680354 , version 1

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Mahdi Ibrahim, Pascal Biwole, Salah Eddine Ouldboukhitine, Fatima Harkouss, Farouk Fardoun. Hyperparameter optimization of artificial neural network in building energy simulation. Conférence IBPSA France 2024 & Ecole thématique SIMUREX, May 2024, La Rochelle - Ile d'Oléron, France. ⟨hal-04680354⟩
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