adaptive behavior satisfaction index (absi) framework for assessing energy efficient building indoor environment: applying kano model

Green building rating systems have limitations in integrating energy efficiency with user satisfaction and emotional and physical well-being. this study has developed the adaptive behavior satisfaction index (absi) analysis framework, which can assess the user satisfaction from cooling adaptive behaviors in green buildings. in particular, the absi analysis framework can measure user satisfaction with respect to thermal adaptive behavior during the design phase of the building life cycle. the cooling absi analysis framework was developed through synectics brainstorming, prototyping system development, and the kano model. the cooling absi analysis framework has three stages; (i) data input stage to set up the purposive sampling plan for the surveys, (ii) data processing stage of applying the kano model, and (iii) data output stage for descriptive and visualized data analysis. it generates a reliable snapshot of the user’s current feelings and perceptions, clustering them into six categories; attractive, must-be there, satisfier, indifferent, reversed, and questionable. to be validated, the framework was applied to a case study (i.e., low energy office (leo) building in putrajaya, malaysia). it showed that, in leo, the feature ‘taking a break and moving to cooler location’ achieved the 100% user satisfaction index, in contrast to decreasing body skin temperature and adjusting the room’s thermostat (5%). furthermore, in leo, the feature ‘drying body skin moisture’ received the highest importance index score (si.i.-c6 = 9.40), followed by ‘adjusting room’s thermostat’ (si.i.-c18 = 9.13), and ‘adjusting air-condition operating hours’ (si.i.-c17 = 9.00). the satisfaction-dissatisfaction scatter plot showed that the ‘drying body skin moisture’ (ssi-c6 = 0.95) and ‘less-sweating lifestyle’ (ssi-c2 = 0.98) features are located in the satisfier cluster. on the other hand, the ‘adjusting finishing material’ (ssi-c10 = 0.73) and ‘adjusting room’s thermostat’ (ssi-c9 = 0.55) features are in the attractive cluster. the scatter plot helps the architects and facility managers understand the cluster of each feature and then decide whether to keep it as-is, improve it, or make an innovative solution for the future towards less-energy consumption and a quality indoor environment for users. furthermore, this study reported the cooling absi analysis framework's feasibility based on the validation and case study results