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An improved weighted average algorithm with Cloud-Based Risk-Conscious stochastic model for building energy

Suraparb Keawsawasvong1, Thira Jearsiripongkul2, Mohammad Khajehzadeh3

  • 1Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand.

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Summary
This summary is machine-generated.

This study introduces a cloud theory-based model for optimizing building energy consumption, using an Improved Weighted Average Algorithm (IWAA) to minimize annual energy consumption (AEC) while accounting for efficiency uncertainties.

Keywords:
Building energy optimizationCloud-Based Risk-Conscious stochastic modelDynamic weight update mechanismImproved weighted average algorithm

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Area of Science:

  • Building energy optimization
  • Stochastic modeling
  • Cloud theory applications

Background:

  • Building energy consumption is a significant factor in global energy usage.
  • Uncertainty in cooling and heating efficiencies poses risks to energy optimization.
  • Existing optimization algorithms may not adequately handle parameter variability.

Purpose of the Study:

  • To develop a cloud theory-based stochastic model for building energy optimization.
  • To minimize annual energy consumption (AEC) in an office building by addressing environmental parameter variability.
  • To introduce an Improved Weighted Average Algorithm (IWAA) with a Dynamic Weight Update Mechanism.

Main Methods:

  • Proposed a cloud theory-based stochastic model.
  • Developed the Improved Weighted Average Algorithm (IWAA) with a Dynamic Weight Update Mechanism.
  • Evaluated the IWAA against benchmark functions and in building energy optimization scenarios under varying weather conditions.

Main Results:

  • The IWAA demonstrated superior performance over WAA, PSO, and WOA, yielding more stable and consistent results for lower AEC.
  • Incorporating uncertainty via cloud theory improved the realism and credibility of energy forecasting.
  • The model effectively balanced exploration-exploitation trade-offs for enhanced optimization.

Conclusions:

  • The proposed IWAA offers a robust approach to building energy optimization, effectively managing uncertainties.
  • Cloud theory integration enhances the reliability of energy forecasting in dynamic environments.
  • The developed model shows significant potential for sustainable and energy-efficient building design.