A new approach to performance-based building design exploration using linear inverse modeling

Abstract

Despite the development of a large number of building performance simulation tools, designers still need a systematic framework appropriate for energy-oriented decision-making in the early stages of design. While the current workflow follows a "forward" modelling procedure in which simulation tools predict the performance of a design, this study proposes an "inverse" procedure that entails a performance objective that estimates design parameters. Using linear inverse modelling, this approach generates plausible ranges for design parameters given a preferred thermal performance. The paper begins by demonstrating that thermal demand in a particular building operation-and-climate condition can be expressed as a linear regression model and then, in two case-studies, uses the regression model to develop an inverse algorithm. After defining energy performance targets as input, users obtain a probabilistic estimate of design parameters as output that represents a large "menu" of feasible design solutions, provides confidence, and embodies the iterative nature of design.

An Incentives Planning Framework for Residential Energy Retrofits: A Life Cycle Thinking-Based Analysis under Uncertainty

“…In contrast to the prescriptive approach, the owners are compelled to adopt energy-conscious behaviour to reach better performance levels to secure higher incentive amounts [29]. However, assessing the pre-and post-retrofit performance of an existing building and predicting the future outcomes of design decisions can be quite challenging when issuing this type of incentive [30].…”

A Data-driven Approach for Sustainable Building Retrofit—A Case Study of Different Climate Zones in China

“…However, these programs require specific building material parameters, detailed HVAC systems, definite operation schedules, and dynamic environmental conditions, but collecting these materials is a tremendous workload for a country without a national database system of building information; meanwhile, building and calibrating a performance simulation model is time-consuming and comprehensive because of the required numerous complicated inputs [37,38]. Due to the lack of a simplified simulation method for the wide range of data-especially for temporal, spatial, and climate variability-energy simulation is often complex and difficult with the existing methods [39,40]. On the other hand, it is impossible to amend the engines of most dynamic simulation tools, since the model calculations are complicated and it is hard to connect control algorithms with those simulation tools [41].…”

A Simulation Method for Measuring Building Physics Properties

“…Despite of this statement, no attempt was made to quantify the effect of thermal energy storage property of the building. Rezaee et al (2019) used linear inverse modelling for performance based design exploration, and established a relationship between a range of design parameters and preferred thermal performance, however no attempt to establish building physics parameters was made. (Abushakra, 1997) used Stepwise Multiple Linear Regression, Fourier series, and Monte Carlo simulation to create an inverse model of a building based on sort term measurements and predict its long term performance before and after retrofit, with prediction errors between 4% and 23%.…”