A review of approaches and applications in building stock energy and indoor environment modelling

Abstract

Current energy and climate policies are formulated and implemented to mitigate and adapt to climate change. To inform relevant building policies, two bottom-up building stock modelling approach: 1) archetype-based and 2) Building-by-building have been developed. This paper presents the main characteristics and applications of these two approaches and evaluates and compares their ability to support policy making. Because of lower data requirements and computational cost, archetype-based modelling approaches are still the mainstream approach to stock-level energy modelling, life cycle assessment, and indoor environmental quality assessment. Building-by-building approaches can better capture the heterogeneous characteristics of each building and are emerging due to the development of data acquisition and computational techniques. The model uncertainties exist in both models which may affect the reliability of outputs, while stochastic archetype models and timeless digital twin model have the potential to address the issue. System dynamics modelling approach can describe and address the dynamics and complexity of often-conflicting policies and achieve co-benefit of multiple policy objectives. This paper aims to provide comprehensive knowledge on building stock modelling for modellers and policymakers, so they could use a building stock model with an appropriate user interface without having to fully understand the underlying algorithms or complexities.

Urban Building Energy Modeling to Support Climate-Sensitive Planning in the Suburban Areas of Santiago de Chile

“…Archetypes are mainly implemented in urban building energy modeling (UBEM), utilizing their annual energy-use intensity (EUI) with the aim of evaluating energy consumption, energy savings after retrofitting measures, and climate change's effects [30]. However, they are also used for life cycle assessment modeling to measure environmental impact and for indoor environmental quality modeling to evaluate indoor conditions and their impact on the occupants.…”

“…This is a mainstream bottom-up approach when a consumption database for buildings is not available for city-scale applications [29]. Currently, accessibility of consumption data is one of the main limitations on energy modeling at an urban scale, and UBEM based on archetypes is widely used due a very limited amount of consumption data to provide, its simple approach and reduced modeling efforts [30]. On the other hand, the heterogeneities in terms of energy use and the possibilities of retrofitting interventions in buildings within complex urban environments cannot be detected [31,32].…”

Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field

“…It has found use in assessing the structural capacity of existing buildings and analyzing the thermal and structural behaviors of building materials [44][45][46]. Agent-based models, known for their effectiveness in studying systems populated by individual, autonomous entities (agents) interacting with each other and their environment, have been deployed to model building energy usage and indoor environments, creating archetypal models based on real buildings [47][48][49]. Meanwhile, neural network models, designed to recognize patterns and relationships in data by mimicking the human brain's operation, are emerging in the field of building stock analysis [50].…”

Application of the Typology Approach for Energy Renovation Planning of Public Buildings’ Stocks at the Local Level: A Case Study in Greece

“…A classification system initially provides building-stock clustering into representative typologies. The necessary KPI database is then produced simply by scaling-up energy analysis results from a strategically selected (or statistically formulated) representative building of each typology to all buildings of the typology; hence, the requirement for detailed technical data collection and energy calculations is significantly reduced as it refers only to the selected representative buildings [10,11]. Pristerà et al [9] also argue that the typology-based approach is more efficient as it simply relies on the scale-up of KPIs from the representative (archetype) buildings to the whole building stock based on the buildings' number or floor area.…”