MULTI-STAGE SENSITIVITY ANALYSIS OF EARLY DESIGN STAGE PARAMETERS FOR PCM-INTEGRATED BUILDINGS

Abstract

Buildings are substantial contributors to greenhouse gas emissions and energy consumption. To meet the global carbon reduction goals, the enhancement of energy-efficiency of the buildings is vital. Early selection of key design parameters during building design offers the best opportunity for achieving energy-efficient buildings. Several studies have highlighted the importance of early design decisions in the building design process. However, the following issues have not been addressed yet: (a) How to develop a systematic framework for multi-stage sensitivity analysis and optimization of buildings to reduce total energy consumption in hot semi-arid climate zones? (b) Which early design stage parameters significantly impact the design of PCM-integrated buildings in hot semi-arid climate zones? (c) How do the early design stage parameters interact with total energy consumption? (d) What is the most effective combination of early design stage parameters that can reduce the total energy consumption of PCM-integrated buildings? (e) Are the optimal solutions for buildings economically feasible? (f) What is the impact of climate change on total energy consumption in PCM-integrated buildings? Thus, this research proposes a multi-stage sensitivity analysis and optimization framework to comprehensively explore the influence and relationship of early design stage parameters on total energy consumption in six cities in the hot semi-arid climate zone. For the multi-stage sensitivity analysis framework, three methods of global sensitivity analysis indices (Standardized rank regression coefficients, Partial rank correlation coefficient, and the Morris method), which are considered to be the most robust and efficient, were selected for the first stage while in the second stage, local sensitivity analysis was applied. The building layout, energy-efficiency measures, and envelope thermophysical properties of passive design measures were used as input parameters, while total energy consumption was selected as the output parameter. Thereafter, Evolutionary Algorithms were used to solve an optimization problem to find the best set of optimal solutions. Finally, an economic assessment of optimal solutions was applied to the current scenario. Based on the multi-stage sensitivity analysis results, the roof solar absorptance, wall solar absorptance, window conductivity, building orientation, and melting temperature of PCM were the most important early design stage parameters influencing total energy consumption. The rankings of certain early design stage characteristics may vary considerably between cities in the same climatic zone. The obtained single-objective optimization results showed that the v total energy consumption was reduced by up to 19.7% and 5.6% for the current and future scenarios, respectively. For the current scenario, the payback period was between 25.7 and 63.7 years, respectively. Conclusively, this research presents design guidelines supported by data and can help engineers, designers, and architects use passive design measures from the early design stages in hot semi-arid climates