Bottom-Up Modeling of Building Stock Dynamics - Investigating the Effect of Policy and Decisions on the Distribution of Energy and Climate Impacts in Building Stocks over Time

In Europe, residential and commercial buildings are directly and indirectly responsible for approximately 30–40% of the overall energy demand and emitted greenhouse gas (GHG) emissions. A large share of these buildings was erected before minimum energy-efficiency standards were implemented and are therefore not energy- or carbon-efficient. Consequently, buildings offer significant potential in terms of energy efficiency and the reduction of GHG emissions compared to the status quo. To make use of this potential at scale, targeted policy measures and strategies are needed that should be based on a quantitative assessment of the feasibility and impact of these measures.Building stock models (BSMs) have long been used to assess the current and future energy demand and GHG emissions of building stocks. Most common BSMs characterize the building stock through the use of archetype buildings, which are taken to be representative of large segments of the stock. The increasing availability of disaggregated datasets—such as building registries, 3D city models, and energy performance certificates—has given rise to building-specific BSMs focusing on describing the status quo as an input to energy planning, primarily on the urban scale. Owing to the availability of building-level data, BSMs can be extended beyond policy advice and urban planning, to the assessment of large building portfolios. Thus far, the advances made in building-specific BSMs on the urban scale have not been transferred to the national scale, where such datasets are often not available. Moreover, the focus on an increasingly detailed description of the existing stock has left approaches for modeling stock dynamics without much development. Stock dynamics, therefore, are still primarily modeled through exogenously defined retrofit, demolition, and new construction rates. This limits the applicability and reliability of model results, as the influence of economic, environmental, or policy factors on stock development is not considered.This thesis addresses these shortcomings and advances modeling practices in BSMs. The thesis with appended papers provides a methodology for how the modeling of national building stock can be further developed in terms of building stock characterization through synthetic building stocks as well as stock dynamics through the use of agent-based modeling. Furthermore, the thesis extends BSM applications to inform the strategic planning of large building portfolios through the integration of a maintenance and renovation scheduling method to project the future development of building portfolios

Identify Optimal Renovation Packages for Residential Buildings: A State-of-the-Art Computational Model

Renovating the existing building stock has a significant potential to achieve the goal of reducing greenhouse gas (GHG) emissions in the European Union. However, a common European renovation project focuses primarily on improving the thermal performance of the building shell by adding insulation to the opaque surfaces and improve the thermal performance of the windows. The potentially positive contribution of renewable energies (RE) in balance with energy efficiency measures is often underestimated. Consequently, a more holistic approach can contribute to a reduction in total net energy demand up to 40-45% for the entire buildings sector. Thus, in order to achieve the goal of GHG emission reduction in an economic most responsible way, the share of RE in a renovation project needs to be increased. However, building renovation projects are becoming - apparently - more complicated if more factors are considered in the planning of a renovation project. Thus, a computational tool for evaluating hundreds of different renovation options, including the implementations of renewable energy resources, to obtain an optimal or nearly optimal set of renovation options is essential. Therefore, a novel planning tool has been developed within the framework of DREEAM project, a project funded by the European Union within the Horizon 2020 research framework. The DREEAM-Tool has been designed in the way that it helps designers and other stakeholders to plan a renovation project of a single building or even on a multi-building scale. The tool was built in the way to optimize the renovation project taking into consideration the most critical factors in planning and decision-making processes, such as the economic or environmental performance. In other words, the tool combines an energy calculation model for a building or multiple building with an economic and environmental assessment to identify and optimize the most beneficial refurbishment solutions. The current study presents the concept of the DREEAM-Tool and shows examples of how the optimal renovation packages of a considered building will be determined and how this will support designers or buildings owners in decision-making processes

Prioritizing deep renovation for housing portfolios

Cost-effectiveness of deep renovation has been assessed thoroughly on a building level. Such studies pro- vide limited guidance when prioritizing renovation measures for a building portfolio. On a stock level, building-stock modelling is commonly used to assess impact of renovation on a national and city level, targeting stakeholders operating at a planning or policy level. However, due to methodological choices and data availability, assessment of property owner portfolios is lacking. The aim of this paper is to cal- culate and spatially differentiate cost-effectiveness of deep renovation using equivalent annual cost and increase in assessed building value for a portfolio owner as a first step in prioritizing deep renovation within a building portfolio. A bottom-up engineering-based model is applied utilizing building-specific information for a municipal housing company portfolio in the City of Gothenburg, Sweden, consisting of 1803 multi-family buildings. Energy demand for space heating and hot-water is calibrated using mea- sured energy use from energy performance certificates. Deep renovation is assessed by applying a pack- age of measures across all buildings. Results show average energy use reduction across the portfolio of 51% to an average cost of 597 EUR/m 2 living area. While average energy cost savings account for 21% of equivalent annual cost, there are seven buildings where more than half the annual equivalent cost of renovation is covered by energy cost savings. Similarly, the distribution of change in assessed build- ing value is large for individual buildings, ranging from 0–23%. Aggregating results to larger areas tend to average out results while differences between individual buildings within areas persists. As such, the cost-effectiveness of deep renovation should be assessed on a building-by-building basis rather than for an area or neighbourhood. The results are intended as a first step in prioritizing deep renovation within a building portfolio and further detailed assessment is needed

Towards agent-based building stock modeling: Bottom-up modeling of long-term stock dynamics affecting the energy and climate impact of building stocks

Buildings are responsible for a large share of the energy demand and greenhouse gas (GHG) emissions in Europe and Switzerland. Bottom-up building stock models (BSMs) can be used to assess policy measures and strategies based on a quantitative assessment of energy demand and GHG emissions in the building stock over time. Recent developments in BSM-related research have focused on modeling the status quo of the stock and comparatively little focus has been given to improving the modeling methods in terms of stock dynamics. This paper presents a BSM based on an agent-based modeling approach (ABBSM) that models stock development in terms of new construction, retrofit and replacement by modeling individual decisions on the building level. The model was implemented for the residential building stock of Switzerland and results show that it can effectively reproduce the past development of the stock from 2000 to 2017 based on the changes in policy, energy prices, and costs. ABBSM improves on current modeling practice by accounting for heterogeneity in the building stock and its effect on uptake of retrofit and renewable heating systems and by incorporating both regulatory or financial policy measures as well as other driving and restricting factors (costs, energy prices)

Policies to decarbonize the Swiss residential building stock: An agent-based building stock modeling assessment

In light of the Swiss government\u27s reduction targets for greenhouse gas (GHG) emissions under the Paris Agreement, this article investigates how and with which policy measures these reduction targets can be met for the Swiss residential building sector. The paper applies an agent-based building stock model to simulate the development of the Swiss residential building stock under three different policy scenarios. The scenario results until 2050 are compared against the reduction targets set by the Swiss government and with each other. The results indicate that while the current state of Swiss climate policy is effective in reducing energy demand and GHG emissions, it will not be enough to reach the ambitious emission-reduction targets. These targets can be reached only through an almost complete phase-out of fossil-fuel heating systems by 2050, which can be achieved through the introduction of further financial and/or regulatory measures. The results indicate that while financial measures such as an increase in the CO2 tax as well as subsidies are effective in speeding up the transition in the beginning, a complete phase-out of oil and gas by 2050 is reached only through additional regulatory measures such as a CO2 limit for new and existing buildings

A service-life cycle approach to maintenance and energy retrofit planning for building portfolios

\ua9 2019 The Authors Residential buildings account for almost a quarter of the total energy use in Sweden and building owners are, therefore, under pressure from policy makers to improve the energy performance of their buildings. Building portfolio owners (BPOs) generally face multiple barriers in energy efficiency investments such as financial constraints and lack of knowledge of the current state when planning energy efficiency measures. This paper presents a method for cost-optimal scheduling of maintenance and retrofit measures on a portfolio level by drawing on research on building stock modeling and maintenance retrofit planning. The method uses a building stock modeling approach to model costs, energy and greenhouse gas emissions (GHG)of a building portfolio and combines this with a method for optimal maintenance and retrofit scheduling in order to forecast and optimize the timing of measures on a building portfolio level. This enables the integrated long-term planning on retrofit investments and reduction of energy demand and GHG emissions for a portfolio of existing buildings. The application to the building portfolio of the municipal housing company of Gothenburg showed that by optimizing the maintenance and retrofit plans, ambitious retrofit measures can be introduced in the majority of the buildings with a positive effect on the service-life cycle costs. Moreover, the method is easily transferable to other building portfolios in Sweden as it builds up on nationally available data sets but is ideally complemented and verified using inspection data and existing maintenance plans of the BPOs in future applications

Diffusion of energy efficiency technologies in European residential buildings: A bibliometric analysis

Many studies have investigated different aspects in the decarbonisation of the European housing stock. However, a comprehensive quantitative analysis of the literature on the diffusion of energy efficiency technologies is still missing. We conducted a bibliometric analysis to better understand the knowledge base in the field energy efficiency technology diffusion in the European residential building stock. After the scanning and screening process, we identified 954 scientific articles pertinent to this topic. Through a co-citation network analysis, we generated a visual knowledge structure of the field and by the further investigation of the bibliography we were able to synthesize the state-of-the-art and answer to our initial research questions. Results of the co-citation network show a scattered and fragmented field in many domains. The descriptive analysis highlights this fragmentation, especially on a cross-country level among EU country members. Findings from this study contribute to map the scientific knowledge base in relation to technology diffusion in European residential building projects, identify relevant topic areas, visualize the links between the topics, as well as to recognize research gaps and opportunities. The methodology utilized in this paper proved to be viable approach to map and characterize the knowledge base within a field and can, therefore, be replicated in upcoming studies with analogous ambitions

Methodologies for Synthetic Spatial Building Stock Modelling: Data-Availability-Adapted Approaches for the Spatial Analysis of Building Stock Energy Demand

Buildings are responsible for around 30 to 40% of the energy demand and greenhouse gas (GHG) emissions in European countries. Building stock energy models (BSEMs) are an established method to assess the energy demand and environmental impact of building stocks. Spatial analysis of building stock energy demand has so far been limited to cases where detailed, building specific data is available. This paper introduces two approaches of using synthetic building stock energy modelling (SBSEM) to model spatially distributed synthetic building stocks based on aggregate data. The two approaches build on different types of data that are implemented and validated for two separate case studies in Ireland and Austria. The results demonstrate the feasibility of both approaches to accurately reproduce the spatial distribution of the building stocks of the two cases. Furthermore, the results demonstrate that by using a SBSEM approach, a spatial analysis for building stock energy demand can be carried out for cases where no building level data is available and how these results may be used in energy planning

Synthetic building stocks as a way to assess the energy demand and greenhouse gas emissions of national building stocks

In Europe, the final energy demand and greenhouse gas (GHG) emissions of residential and commercial building stocks account for approximately 40% of energy and emissions. A building stock model (BSM) is a method of assessing the energy demand and GHG emissions of building stocks and developing pathways for energy and GHG emission reduction. The most common approach to building stock modeling is to construct archetypes that are taken to representing large segments of the stock. This paper introduces a new method of building stock modeling based on the generation of synthetic building stocks. By drawing on relevant research, the developed methodology uses aggregate national data and combines it with various data sources to generate a disaggregated synthetic building stock. The methodology is implemented and validated for the residential building stock of Switzerland. The results demonstrate that the energy demand and GHG emissions can vary greatly across the stock. These and other indicators vary significantly within common building stock segments that consider only few attributes such as building type and construction period. Furthermore, the results indicate a separation of the stock in terms of GHG emissions between old fossil fuel-heated buildings and new and refurbished buildings that are heated by renewable energy. Generating a disaggregated synthetic building stock allows for a discrete representation of various building states. This enables a more realistic representation of past building stock alterations, such as refurbishment, compared with commonly used archetypes, and not relying on more extensive data sources and being able to accommodate a wide variation of data types. The developed methodology can be extended in numerous manners and lays groundwork for future studies

Challenges and Lessons Learned in Applying Sensitivity Analysis to Building Stock Energy Models

Uncertainty Analysis (UA) and Sensitivity Analysis (SA) offer essential tools to determine the limits of inference of a model and explore the factors which have the most effect on the model outputs. However, despite a well-established body of work applying UA and SA to models of individual buildings, a review of the literature relating to energy models for larger groups of buildings undertaken by Fennell et al. (2019) highlighted very limited application at larger scales. This contribution describes the efforts undertaken by a group of research teams in the context of IEA-EBC Annex 70 working with a diverse set of Building Stock Models (BSMs) to apply global sensitivity analysis methods and compare their results. Since BSMs are a class of model defined by their output and coverage rather than their structure and inputs, they represent a diverse set of modelling approaches. Key challenges for the application of SA are identified and explored, including the influence of model form, input data types and model outputs. This study combines results from 7 different modelling teams, each using different models across a range of urban areas to explore these challenges and begin the process of developing standardised workflows for SA of BSMs