Abstract
Rising demand and limited production of electricity are instrumental in spreading the awareness of cautious energy use, leading to the global demand for energy-efficient buildings. This compels the construction industry to smartly design and effectively construct these buildings to ensure energy performance as per design expectations. However, the research tells a different tale: energy-efficient buildings have performance issues. Among several reasons behind the energy performance gap, occupant behavior is critical. The occupant behavior is dynamic and changes over time under formal and informal influences, but the traditional energy simulation programs assume it as static throughout the occupancy. Effective behavioral interventions can lead to optimized energy use. To find out the energy-saving potential based on simulated modified behavior, this study gathers primary building and occupant data from three energy-efficient office buildings in major cities of Pakistan and categorizes the occupants into high, medium, and low energy consumers. Additionally, agent-based modeling simulates the change in occupant behavior under the direct and indirect interventions over a three-year period. Finally, energy savings are quantified to highlight a 25.4% potential over the simulation period. This is a unique attempt at quantifying the potential impact on energy usage due to behavior modification which will help facility managers to plan and execute necessary interventions and software experts to develop effective tools to model the dynamic usage behavior. This will also help policymakers in devising subtle but effective behavior training strategies to reduce energy usage. Such behavioral retrofitting comes at a much lower cost than the physical or technological retrofit options to achieve the same purpose and this study establishes the foundation for it.
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)
Cited by
32 articles.
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