Worldwide, the building sector is responsible for consuming more than 36% of the final global energy and produces 39% of carbon dioxide emissions. Accordingly, sustainable retrofit is an important method to achieve energy reduction and sustainable development. However, the lack of information on retrofit technologies and their benefits trigger stakeholder's opposition to retrofit actions. The Energy Performance Certificate tool can be used to overcome the knowledge gap and boost energy saving by strengthening its recommendation report with retrofit technologies for energy performance. Therefore, this paper attempts to determine the best retrofit technologies to be highlighted in the Energy Performance Certificate's recommendation report by considering stakeholder's opinions. For this purpose, a model based on Quality Function Deployment has been developed. The model analyzes the data regarding stakeholder's expectations when deciding to retrofit, and the potential retrofit technologies used. To validate the applicability of the proposed model, a case study was conducted in Romania. The findings are expected to contribute to improving the quality of the Energy Performance Certificate, as reflecting stakeholder's opinions combined with sustainable concepts to achieve significant energy savings.
With an increasing demand for quieter residential environments, impact sound insulation for floating floors is gaining importance. However, existing methods for estimating the performance of heavy impact sound insulation are limited by their inability to comprehensively analyze various types of floating floors, as well as difficulties mathematically determining the input force of the reference source for heavy impacts. To overcome these limitations, this study proposes empirical models for estimating the sound insulation performance of floating floors under heavy impacts. The proposed models are then validated; the model with the highest accuracy exhibits an average estimation error of 2.73 dB at 50-630 Hz. The proposed models exhibit better accuracies than existing analytical models for frequencies below 100 Hz, where the estimation errors of the analytical models were large. Thus, the proposed models may help reduce errors in analytical estimates or when estimating a single numerical quantity for sound insulation rating during the design stage of multifamily housing.
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