Passive energy consumption optimisation strategy for buildings

Buildings serve as the cornerstone of human civilisation, providing shelter, workspaces, and gathering places essential for fostering social, economic, and cultural activities. However, they simultaneously contribute to environmental degradation in several ways, e.g., consuming a significant amount of the
total produced energy and global resources. The substantial energy demands of active systems, such as energy recovery ventilation systems for heating and cooling, have increasingly directed research efforts towards the investigation of passive design strategies in recent years. However, the selection of passive strategies for buildings involves the consideration of various sustainability criteria, which span across
social, technological, environmental, and economic domains, if a truly sustainable selection is desired. Therefore, it is necessary to utilise multiple criteria decision-making (MCDM) techniques for selecting passive strategies for buildings.
The existing studies within the relevant body of knowledge have mainly focused on a limited number of criteria for selecting passive strategies, especially energy saving and cost, leading to the neglect of other crucial aspects of sustainability concepts. Also, dated and crisp versions of MCDM techniques were used, which are limited in their capability to address uncertainties, thereby leading to ambiguities
in solving MCDM problems. Considering the existence of even more gaps within the relevant existing body of knowledge (e.g., limited information about the drivers and barriers to the adoption of passive strategies, and lack of the existence of a decision support tool which can ease such selection for practitioners), this study focuses on comprehensively investigating the selection of passive strategies for buildings, with a particular focus on the buildings located in the United Kingdom.
The study initially identifies the existing passive energy consumption optimisation strategies, the sustainability criteria involved in their selection, and the MCDM techniques that aided the selection process by conducting a systematic literature review. This is followed by a comprehensive description of the characteristics of different MCDM techniques for the selection of passive strategies for buildings.
Then, the best MCDM technique for selecting passive strategies for buildings is identified. Focusing on the buildings located in the United Kingdom, the next phase of the study identifies the best passive energy consumption optimisation strategies using a hybridised fuzzy extension of the investigated MCDM techniques in the previous phase. Then, the drivers and barriers to the adoption of passive energy consumption optimisation strategies for UK buildings are identified and discussed.
The final phase of the study utilises all the obtained results from the previous stages to develop a knowledge-based decision support tool (KB-DST) termed as “Passive strategy assessment tool (PSAT)” using Python. This tool can considerably facilitate the selection of passive strategies for practitioners.