Analysis of Occupant Behaviours in Energy Efficiency Retrofitting Projects

Abstract

This review of studies into Energy Efficiency Retrofitting (EER) has shown the practice of EER to be a key factor in sustainability regeneration. Thus, the retrofitting practice itself (the way it is organised) has received increasing attention from both practitioners and researchers, and studies are now addressing some issues that are affecting the retrofit level of achievement. Most of the risks which lead to low retrofit development are related to owners. This paper aims highlight the role of the occupants in achieving the goals of EER. It is found that: a) the early involvement of occupants in the design and construction stage, b) mutual engagement, and c) an integral approach that involves the occupants are the key to motivate EER decisions from these same occupants. It follows that this involvement, including the demographic characteristics of the occupants, such as their culture, habits, preferences, awareness towards energy saving and socio-economic factors, are indeed effective in influencing the energy-related behaviours of these occupants. Moreover, other factors, such as space-heating behaviour, presence/absence of the occupants, control level of the equipment and window, and lighting control behaviour, are all effective factors in the energy performance of the buildings. Hence, socio-technical advancements, co-design processes and effective energy efficiency policies are recommended strategies to: a) improve occupants’ behaviours; and b) increase their participation in EER projects.

A Review of How Occupant Behaviour Determines the Energy Efficiency of Green Buildings: A Case of Office Buildings

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Combining CAD Modeling and Simulation of Energy Performance Data for the Retrofit of Public Buildings

“…The third phase is the evaluation of the potential of each intervention, while the fourth phase is dedicated to the implementation of the renovation solutions, which are finally validated by the results in the fifth phase [11]. Numerous studies already provide tools and context for reducing energy consumption around the world [3][4][5][6][7][8][9][10][11][12][13][14]. Retrofitting existing structures can significantly reduce energy consumption and greenhouse gas emissions worldwide.…”

“…Therefore, they propose a machine learning approach to indicate optional interventions. Numerous studies already provide tools and context for reducing energy consumption around the world [3][4][5][6][7][8][9][10][11][12][13][14]. Retrofitting existing structures can significantly reduce energy consumption and greenhouse gas emissions worldwide.…”