Building Stock Data Research Articles

Identifying Hard-to-Decarbonize houses from multi-source data in Cambridge, UK

As the urban population continues to expand and is expected to comprise 80% of the total population in 2050, it is crucial to ensure the sustainability and energy efficiency of cities. Among all the homes globally, Hard-to-Decarbonize (HtD) buildings are estimated to be a quarter of them. Identifying the HtD houses and proposing strategies for these houses is important to reach the global net zero target. However, the study of HtD houses has historically been neglected. Previous studies mainly focus on simulating, predicting and understanding attributes that are directly related to energy usage and efficiency. In this research, a methodology for identifying HtD buildings with publicly available data is proposed and tested in Cambridge, UK. A dataset of HtD houses in Cambridge is organized, with criteria derived from the Energy Performance Certificate (EPC), which results from detailed inspections of houses. Street view images (SVI), aeriel view images (AVI), land surface temperature (LST), and building stock data are used together for the prediction with deep learning. The classification precision for HtD buildings is able to achieve 82%. This study also explores the minimal data needed for the high-accuracy prediction of HtD houses. Results show that SVI contributes the most to the prediction.

A new method for estimating the smart readiness of building stock data using display energy Certificate data

The Smart Readiness Indicator (SRI) was introduced by the revised Energy Performance of Buildings Directive (EPBD) 2018/844/EU, as a voluntary scheme for rating the smart readiness of buildings. The methodology addresses the benefits of smart technologies and their functionalities in buildings to offer low-energy, healthier, and environmentally comfortable buildings that balance the needs of occupants and the energy grid. This is a case study implementation of the SRI calculation to the university campus scale. The SRI methodology is applicable on a building-by-building basis; however, the assessment of large stocks and energy hubs is not addressed and will be the focus of this study. Other studies have proposed a method to extend the calculation to building clusters which, however, involves assessing all the buildings in the cluster in detail. The current case study extends to a much larger number of buildings, therefore, the assessment of each individual building was not considered effective. A critical implementation of the SRI calculation is presented here that leverages publicly available information like Display Energy Certificate (DEC) advisory recommendation reports for a university campus. A complex building is used to verify the proposed method based on the SRI detailed methodology. The proposed method provides a solution to the large-scale processing of building information across multiple complex buildings, hence benefiting decision-makers early on. Furthermore, the method benefits from the inclusion of building-specific recommendations based on DEC surveys. For the 98 buildings examined, a campus SRI score range has been estimated and focus areas have been identified as the foundation of smart energy and net-zero transition pathways.

Dynamically distributed district heating for an existing system

The study in hand introduces the concept of dynamically distributed district heating. The concept addresses the challenges related to transforming an existing 3rd generation district heating system into a 4th generation system, one area at a time. It enables a cost-efficient option for introducing low-temperature distribution and new distributed heat supply while preserving the advantages of an efficient, more centralised system. The concept includes new large-scale heat storage capacity in areas on the outskirts of the network or within otherwise suitable locations, charged during summer when low-cost heat is commonly available. These areas also have new distributed heat supply. The areas are run in an island-mode during the heating season, i.e. disconnected from the main system. The study presents a preliminary analysis of the concept using Helsinki district heating system as a case study based on open data on district heating demand, building stock data and optimisation modelling of the district heating system for assessing the heat supply costs.

Optimization of the refurbishment of German single family homes based on construction era

This publication devises a method which formulates a national strategy for the renovation of single family houses by treating each era of initial construction independent of all the others, and applies a universally adoptable procedure to evaluate the cost efficiency of carbon emission reduction. A multi-objective optimization was conducted which varied the building envelope and energy systems while optimizing for annual cost and carbon emissions. The optimization was carried out in oemof.solph and PyGMO on typical German building stocks from 11 different construction eras between 1860 and 2020. The buildings were modeled in TEASER using TABULA building stock data. The results indicate that post-war era construction has the greatest improvement potential by saving over 16 tons of CO2/yr with respect to its business as usual case. The recommended solutions for each construction era have an investment cost to emission reduction ratio which is 25% better than those of the current efficiency subsidies.

OpenStreetMap Data as a Basis for Official Geospatial Data: An Investigation into Updating the Building Stock Data for Municipalities in Baden-Württemberg, Germany

OpenStreetMap Data as a Basis for Official Geospatial Data: An Investigation into Updating the Building Stock Data for Municipalities in Baden-Württemberg, Germany

Renovation wave of the residential building stock targets for the carbon-neutral: Evaluation by Finland and Türkiye case studies for energy demand

The recent EU energy efficiency goals and renovation wave have called attention to the building stock's energy-demand-saving potential. Finland and Türkiye, with two contrasting perspectives on climate zones and building typologies in the EU, were chosen to analyse energy-demand potential. The study's scope was restricted to the residential building stock, a significant portion of the overall building stock. The latest residential building stock data and regulations history was gathered via a bottom-up approach. Then three representative buildings for each typology were energy demand simulated for climatic regions and periods. Thirdly, the area-weighted energy needs difference values for each decade (1960–2020) and climatic zone were multiplied with the building stock data to predict saving potential. Fourth, the impact of factors on energy demand was assessed using full-factorial analysis. Lastly, the main factors were parametrically simulated and compared with mandatory limits. The substantial energy-demand-saving potential existed in both countries. The findings highlighted the following: (1) the energy demand saving potential for the Finnish and Turkish building stocks were calculated as 10,176.9 GWh/year (30 %) and 124,889.3 GWh/year (50 %), respectively; (2) energy demand in Finland is dependent strongly on the climatic zone; (3) the optimum thermal insulation thickness for Finnish buildings is likely to be at the current level or even below it, while Türkiye's insulation limits are climatic zone based but not sufficient for carbon neutral; (4) Surprisingly for both countries, the most important factors to consider when renovating buildings were the thermal insulation level of the walls and roof in single-family buildings and the thermal insulation level of the walls in multi-family buildings and the airtightness of the building envelope. It should be noted that for Finland, building energy efficiency regulations will differ on the climate zone. Türkiye's newly published NZEB values should become mandatory lower limits for all residences because current legislative limits are insufficient and date back to the 2000s.

EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings

Building stock management is becoming a global societal and political issue, inter alia because of growing sustainability concerns. Comprehensive and openly accessible building stock data can enable impactful research exploring the most effective policy options. In Europe, efforts from citizen and governments generated numerous relevant datasets but these are fragmented and heterogeneous, thus hindering their usability. Here, we present eubucco v0.1, a database of individual building footprints for ~202 million buildings across the 27 European Union countries and Switzerland. Three main attributes – building height, construction year and type – are included for respectively 73%, 24% and 46% of the buildings. We identify, collect and harmonize 50 open government datasets and OpenStreetMap, and perform extensive validation analyses to assess the quality, consistency and completeness of the data in every country. eubucco v0.1 provides the basis for high-resolution urban sustainability studies across scales – continental, comparative or local studies – using a centralized source and is relevant for a variety of use cases, e.g., for energy system analysis or natural hazard risk assessments.

Provision of multiple services with controllable loads as multi-area thermal energy storage

Power systems are experiencing a decrease of synchronous generation along with increased penetration of inverter based renewable generation leading to reduced system inertia and a need for flexible resources. Non-generating resources such as thermostatically controlled loads (TCLs) are flexible due to their thermal energy storage capacity. When aggregated, TCLs can arbitrage energy prices and provide reserves to the power system. We approach the operational flexibility of the TCLs by modeling a risk-averse aggregator that controls decentralized TCLs and aims to maximize its own profit. The high number and low power rating of residential TCLs makes it difficult to model and assess their flexibility potential on national level. Thus, we make use of a high-level thermal energy storage model for aggregations of TCLs to quantify their flexibility potential. We present a method to aggregate temperature, TCL parameters, and building stock data into a thermal battery equivalent. We propose a multi-period multi-market multi-zonal two-stage chance constrained rolling horizon optimization problem formulation for the risk-averse day-ahead self-scheduling problem of a price-taker TCL aggregator bidding in energy and reserve markets under uncertainty and recast the problem as a linear program. We perform several case studies in the Swedish power system based on a survey of single- and two-family dwellings with electric heating and assess the flexibility potential. Additionally, a sensitivity analysis provides insights regarding market design and policy implications.

Using Real Building Energy Use Data to Explain the Energy Performance Gap of Energy-Efficient Residential Buildings: A Case Study from the Hot Summer and Cold Winter Zone in China

The International Energy Agency (IEA) emphasizes that using real building energy use data (RBEUD) to reflect the actual condition of buildings and inform policy-making is the most effective way to reduce buildings’ carbon emissions. However, based on IEA’s evaluation, regional and national building stock data are limited and lacking. Especially for China, the lack of RBEUD in buildings has limited our ability to address the energy performance gap (EPG). In this research, EPG refers to the difference between regulated energy consumption by design standards and actual energy usage. EPG makes it difficult to develop buildings that are energy-efficient. Therefore, this study aims to gather and analyze RBEUD in order to understand the role of occupants’ behavior in explaining the EPG of energy-efficient residential buildings in China. The results suggest that the actual consumption of residential buildings is less than 1/5–1/3 of the theoretical limits. The heat pump and air conditioner’s actual schedules and setpoint settings are the significant drivers that explain the EPG. In addition, the presentation of a database of 1128 households provides actual usage behavior parameters for policy-makers to improve the accuracy of building energy forecasting models.

Life cycle GHG emissions of the Austrian building stock: A combined bottom-up and top-down approach

Construction and operation of buildings are responsible for 37% of global greenhouse gas (GHG) emissions. In contrast, the Austria’s National Inventory Report attributes a mere 10% of national emissions to buildings – including only direct operational emissions of residential and service sector buildings. This narrow definition of the buildings sector neglects important environmental hotspots attributable to building-related life cycle emissions and calls for a comprehensive analysis of GHG emissions of Austrian buildings. In this study, we assess annual building related GHG emissions for the Austrian building stock from a full life cycle perspective (i.e. including operational and embodied emissions). For embodied emissions, we model emissions using both a process-based and an input-output based life cycle assessment (LCA) approach. Building LCA case studies and statistical building stock data are used to estimate embodied emissions from a bottom-up perspective, which are complemented by estimated emissions from the input-output based LCA approach. Our work illustrates the importance of adopting a life-cycle perspective on building-related emissions to inform the different stakeholders and advance climate action in the built environment. While both the chosen system boundaries and methods significantly determine the results, we argue that emission reduction measures should be based on a comprehensive system boundary of building-related emissions to contribute towards the achievement of a climate-neutral built environment and the stringent climate targets. By adding indirect emissions and non-residential buildings to the officially reported building emissions, the operational emissions alone increase by a factor of 2.4. As expected, the process-based LCA yields lower embodied emissions than the input-output based approach. Depending on the method, they can be responsible for up to 40% of total buildings related emissions. Summing up, total buildings related emissions rise by a factor of 3 to 4 when extending the system boundaries to comprise the whole area of action buildings, and go from 7 Mt CO2-eq/a (direct operational emissions, 10% of national emissions), to 22-31 Mt CO2-eq/a for the case of Austria.

Discovering, processing and consolidating housing stock and smart thermostat data in support of energy end-use mapping and housing retrofit program planning

• Systematic workflow to extract energy-related information from different buildings related datasets is developed. • Different datasets are considered, processed, and information related to building energy simulation are extracted. • Suggestions to improve the building and heating permit data collection at municipality level are made. • Recommendations for effective housing retrofit and energy planning are made. There is growing interest in energy mapping amongst municipal planners and policymakers to accelerate greenhouse gas (GHG) emissions reduction program implementation. Responding to this interest partly involves addressing challenges related to building stock data collection and processing. The present study, carried out in conjunction with Natural Resources Canada's Canadian Energy End-use Mapping (CEE Map) project, aims to develop the inputs for housing energy modeling and mapping using property assessment data, building, heating permit data, and smart thermostat data. In this context, a systematic workflow is presented to extract useful information from various data sources to support housing energy simulations and municipal retrofit program planning. Permit data analysis supported refinement of housing data in Kelowna's urban digital twin. Results from building permit data analysis serve to update housing attributes including construction year and dwelling type. Results from heating permit analysis suggest that 17.5% of Kelowna dwellings could be potential candidates for heating system upgrades. Regarding thermostat setpoint temperature and occupancy for energy simulations, results obtained from smart thermostat data analysis were compared with EnerGuide Rating System (ERS) assumptions to investigate the potential improvements that can be made in energy simulation inputs. Comparative results indicated variations of up to 2 °C between smart thermostat data and EnerGuide assumptions for thermostat setpoint temperatures. Also, smart thermostat data suggests that 87% of dwellings were occupied for more than 50% of the time, whereas in ERS, occupancy is assumed to be 50%. Together, the overall data workflow and the detailed investigation of different datasets contributes to the development of a best practice methodology for housing energy modeling and mapping for municipalities in support of GHG emission reductions. Further, recommendations for permit data collection and scope for future research works are provided.

Accuracy of FEMA-Hazus Single-Family Residential Damage Exposure Data in Houston: Implications for Using or Correcting the Hazus General Building Stock

AbstractSingle-family residential building damage exposure data from the FEMA-Hazus general building stock data set (HGBS) are compared with corresponding Harris County (Houston, Texas) assessor da...

Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network

Energy benchmarking of buildings has an important role in improving energy performance by establishing a reference for the energy efficiency of the building stock. The simulation of archetypes followed by a generalisation model has been widely used to obtain benchmarks. However, even though archetypes summarise the main features of the building stock, the uncertainties must be accounted for in the modelling process. Moreover, testing the response of the benchmarking model using the actual building stock data supports the reliability of the method. This paper aims to propose an innovative framework to reduce the uncertainty of archetypes for benchmarking buildings. A standard framework for data compiling is proposed and an assessment of the uncertainty of variables using entropy and cluster analysis allowed to obtain representative archetypes. An Artificial Neural Network (ANN) was used as a predictive tool, and it was applied to benchmark a sample of actual buildings. Also, the simulation outcomes were used to determine energy end-uses according to the climatic zones. The framework proposition is presented alongside with a practical application. The result is an unprecedented benchmarking model: the archetype considers more variation in parameters of the building stock with higher uncertainties. Additionally, the modelling process showed to be robust for combining different datasets, and the ANN achieved high-performance metrics. Conclusion indicates the potential of using the framework for other typologies. Moreover, the framework demonstration is used for a school stock in Brazil, showing a trend to the inefficiency while a specific case study was explored, showing the potential of the method to find faults in the building energy use.

Bayesian Inference of Dwellings Energy Signature at National Scale: Case of the French Residential Stock

Cities take a central place in today’s energy landscape. Urban Buildings Energy Modeling (UBEM) is identified as a promising approach for energy planning and optimization in cities and districts. It generally relies on the use of Building Archetypes, i.e., simplified deterministic models for categorized building typologies. However, this implies large assumptions which may accumulate and induce significant bias on energy consumption estimates. In this work, we address this issue with static stochastic models whose parameters are inferred over national thermo-energy data using Bayesian Inference. We analyze inference results and validate them with a panel of standard indicators. Then, we provide comparative results with deterministic building archetypes and stock data from the TABULA European project. Comparisons between heat loss coefficients show relative coherence between building categories, but highlight some significant bias between both approaches. This bias is also shown in the comparative result of a Monte Carlo simulation using inferred stochastic models for a 10331 dwellings stock. In conclusion, inferred stochastic models show interesting insights over the French dwellings stock and potential for district energy simulation. All code and data involved in this study are released in an open repository.

Energy models for cost-optimal analysis: Development and calibration of residential reference building models for Northern Cyprus

Ambitious energy efficiency goals constitute an important roadmap towards attaining a low-carbon society. Thus, various building-related stakeholders have introduced regulations targeting the energy efficiency of buildings. However, some countries still lack such policies. This paper is an effort to help bridge this gap for Northern Cyprus, a country devoid of building energy regulations that still experiences electrical energy production and distribution challenges, principally by establishing reference residential buildings which can be the cornerstone for prospective building regulations. Statistical analysis of available building stock data was performed to determine existing residential reference buildings. Five residential reference buildings with distinct configurations that constituted over 75% floor area share of the sampled data emerged, with floor areas varying from 191 to 1006 m2. EnergyPlus models were developed and calibrated for five residential reference buildings against yearly measured electricity consumption. Values of Mean Bias Error (MBE) and Cumulative Variation of Root Mean Squared Error CV(RMSE) between the models’ energy consumption and real energy consumption on monthly based analysis varied within the following ranges: (MBE)monthly from –0.12% to 2.01% and CV(RMSE)monthly from 1.35% to 2.96%. Thermal energy required to maintain the models' setpoint temperatures for cooling and heating varied from 6,134 to 11,451 kWh/year.

Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling

Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R2 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario.

Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

Automatic and rapid calibration of urban building energy models by learning from energy performance database

Urban building energy modeling (UBEM) is attracting increasing attention in the energy modeling filed. Unlike modeling a single building using detailed building systems information, UBEM generally uses limited high-level building stock data to infer default assumptions about building characteristics and operations. This practice inherently brings uncertainty to UBEM. This study introduced a novel method of automatic and rapid calibration of UBEM based on the annual electricity and natural gas energy use data by learning the correlations between crucial model input parameters and the building energy use from the reference building models. A case study was presented to calibrate 72 large office buildings built before 1978 in San Francisco. Seventeen model parameters were selected and Monte Carlo sampling was used to create 1000 samples that reasonably represent the parameter space. Then 1000 simulations were performed for the reference building model to create an energy performance database. The results showed that by learning from the energy performance database, it took less than four simulation runs on average to calibrate a building model. After the calibration, the distributions of each parameter were obtained to replace their single predefined default values. For example, the default lighting power density of 21.39 W/m2 was calibrated to be 7.50 W/m2 on average. The case study successfully demonstrated the effectiveness of the novel calibration method for UBEM in the mild climate. The method will be further tested in future for other climate zones and other building types.

Open Source Data for Gross Floor Area and Heat Demand Density on the Hectare Level for EU 28

The planning of heating and cooling supply and demand is key to reaching climate and sustainability targets. At the same time, data for planning are scarce for many places in Europe. In this study, we developed an open source dataset of gross floor area and energy demand for space heating and hot water in residential and tertiary buildings at the hectare level for EU28 + Norway, Iceland, and Switzerland. This methodology is based on a top-down approach, starting from a consistent dataset at the country level (NUTS 0), breaking this down to the NUTS 3 level and further to the hectare level by means of a series of regional indicators. We compare this dataset with data from other sources for 20 places in Europe. This process shows that the data for some places fit well, while for others, large differences up to 45% occur. The discussion of these results shows that the other data sources used for this comparison are also subject to considerable uncertainties. A comparison of the developed data with maps based on municipal building stock data for three cities shows that the developed dataset systematically overestimates the gross floor area and heat demand in low density areas and vice versa. We conclude that these data are useful for strategic purposes on aggregated level of larger regions and municipalities. It is especially valuable in locations where no detailed data is available. For detailed planning of heating and cooling infrastructure, local data should be used instead. We believe our work contributes towards a transparent, open source dataset for heating and cooling planning that can be regularly updated and is easily accessible and usable for further research and planning activities.

A data-driven approach for multi-scale building archetypes development

Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings’ physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale.This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions.