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Integrative soft computing approaches for optimizing thermal energy performance in residential buildings.

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This study introduces novel hybrid algorithms using artificial neural networks and five optimizers to predict building thermal load. The Beluga whale optimization algorithm demonstrated the highest accuracy in estimating annual thermal energy demand.

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Area of Science:

  • Building energy performance analysis
  • Computational intelligence in engineering
  • Sustainable building design

Background:

  • Accurate prediction of building thermal load is crucial for optimizing building design and energy efficiency.
  • While machine learning models are used, advanced techniques for thermal load estimation require further investigation.

Purpose of the Study:

  • To introduce and evaluate novel hybrid algorithms for estimating building thermal load.
  • To compare the performance of artificial neural networks integrated with five different optimization algorithms.

Main Methods:

  • Developed hybrid predictive models combining artificial neural networks with Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO).
  • Assessed model accuracy using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2).
  • Ranked the performance of the integrated algorithms based on predictive accuracy.

Main Results:

  • All hybrid models achieved high accuracy, with relative errors below 5% (MAPE) and correlations above 92% (R2).
  • The Beluga whale optimization (BWO) algorithm demonstrated the highest accuracy in predicting annual thermal energy demand.
  • The Archimedes optimization algorithm (AOA) and snake optimizer (SO) showed the second-best performance, followed by forensic-based investigation (FBI) and transient search algorithm (TSO).

Conclusions:

  • The proposed hybrid algorithms, particularly those integrating artificial neural networks with advanced optimizers like BWO, are effective for reliable building thermal load estimation.
  • The findings provide insights into selecting appropriate artificial intelligence techniques for energy performance analysis in complex buildings.
  • The study highlights the potential of novel optimization algorithms in enhancing the accuracy of building energy simulations.