Cost effective indoor HVAC energy efficiency monitoring based on intelligent decision support system under fermatean fuzzy framework

Document Type : Article

Authors

1 Department of Mathematics, University of the Punjab, Lahore, Pakistan

2 Institute of Energy & Environmental Engineering, University of the Punjab, Lahore, Pakistan

3 Department of Electrical Engineering, University of Cape Town, South Africa

Abstract

The heating, ventilation, and air conditioning (HVAC) control system is responsible for the efficient building energy system. Indoor energy consumption patterns can be monitored and reduced intelligently. Occupancy information plays a vital role to save a reasonable amount of energy. Traditional energy monitoring and control systems can be improved with the installation of the occupancy monitoring system which will consist of a network of sensors and cameras. In this research work, we propose a new and revolutionary convolutional neural network (CNN) based on real-time camera occupancy detection and recognition techniques across different sorts of sensors that provide realistic low-cost energy-saving solutions with robust graphical processing units (GPUs). This occupancy information will decide the energy behaviour inside buildings. Decision-making tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we introduce and develop the "Fermatean fuzzy prioritized weighted average and geometric operator". These aggregation operators (AOs) are a modern approach to modelling complexities in decision-making. In the end, we give an algorithm for an intelligent decision support system (IDSS) using proposed AOs to compare our CNN based method with other existing sensors techniques.

Keywords

References

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Scientia Iranica
Volume 30, Issue 6
Transactions on Computer Science & Engineering and Electrical Engineering (D)
November and December 2023
Pages 2143-2161

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History

  • Receive Date: 05 October 2021
  • Revise Date: 20 December 2021
  • Accept Date: 23 May 2022

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