Climate robust buildings: towards buildings with a robust energy performance under climate change

Abstract

Building performance simulation (BPS) is a powerful tool allowing building designers to estimate the behavior of buildings and assess the impacts of their design decisions on their performance. BPS requires large number of input variables, of which some can be predicted during design phase with reasonable certainty such as thermal properties of materials and building dimensions, and some are difficult to be predicted, such as climate and occupancy. Climate as an input variable in BPS is the main theme of this PhD work. Climate conditions are key input variables for BPS, but traditionally and for simplification, a typical climate condition is used to represent the most likely condition that a building will experience. Such approach results for the final designs to be sensitive to variation of climate conditions and even fail to provide expected performance when the conditions are beyond typical ranges. Such sensitivity of buildings to atypical climate conditions is becoming more critical considering that due to climate change the frequency and intensity of extreme climate conditions is increasing. This thesis provides an overview of the risks induced by climate change on the performance of buildings and provide a method for protection against future climate uncertainty. The first step towards protection against climate uncertainty is to identify the climate conditions that a building might face during its life span. The work identifies a prospect of climate conditions for built environment as: climate normals or typical climate conditions and climate extremes. Climate extremes are distinguished into two: foreseeable extreme conditions and unforeseeable extreme events. It further discusses to which extent these conditions can be considered during the design phase of buildings. After identifying the possible climate conditions, a work was set to create a framework that conceptualise protection for buildings against all these conditions. After reviewing the concepts and definitions provided in the literature, the two concepts of «robustness» and «resilience» were found appropriate for the aimed framework. According to the defined framework, the concept of robustness is the most proper to deal with typical and foreseeable extreme climate conditions, where in this concept the main focus is on reducing the sensitivity of performance under presence of source of uncertainty. The discussion on protection against unforeseeable extreme events falls into the concept of resilience, where withstanding and recovery mechanisms should be considered and was out of scope of this thesis. Based on the framework a climate robust building is “a building that, while in operation, can provide its performance requirements with a minimum variation under typical and foreseeable extreme climate conditions“ In the second step, a total of 74 representative weather files are synthesized for city of Geneva to account for future foreseeable extreme conditions together with typical climate conditions. The aim is to investigate the impacts of these conditions on the energy performance of single buildings and their combination to create a virtual neighborhood. The results showed, depending on the type of building, the relative change of peak load for cooling demand under near future can be up to 28.5% higher for extreme conditions compared to typical conditions. Furthermore, the results for the neighborhood demonstrate the critical situation that an energy network may face due to increased peak load under extreme climatic conditions. It is concluded that only those weather files that take into consideration both typical and extreme conditions are the most reliable for providing representative boundary conditions to test the energy robustness of buildings under future climate uncertainties. In the final step, a method is proposed in this work, in which three future weather files including typical, extreme warm and extreme cold conditions are used in a simulation-based optimization process. The method allows architects and engineers to effectively consider future climate uncertainties during the design phase and achieve solutions with robust energy performance against these uncertainties. Using only three weather files make the process feasible and computationally inexpensive. To test the effectiveness of the method, the primary energy use of an obtained optimum solution is calculated for the 74 weather files. According to the results, the performance of the optimum solution not only has 81.5% lower variation (less sensitivity to climate uncertainty) but at the same time 14.4% lower mean value of energy use in comparison to a solution that is compliant with a recent construction standard (ASHRAE 90.1-2016). Less sensitivity to climate uncertainty means better robustness against climate change and simultaneously keeping a high performance. The simplicity and the low computational demand of the process ascertain the feasibility and applicability of this method. The approach can be used at any stage of design process and can help architects and engineers to improve robustness of their design against future climate uncertainties.