This study investigates the diagnostic capabilities of a Diagnostic Bayesian Network (DBN) for air handling unit (AHU) components, particularly focusing on the heat recovery wheel (HRW) and heating coil valve (HCV). Unlike data-driven methods relying heavily on high-quality labeled data, this knowledge-based DBN is more suitable for real-world applications, where labeled faulty and normal data are hard to obtain. Notably, existing studies predominantly concentrate on developing DBN for AHU with recirculated air, neglecting thorough investigations into AHU with HRW, a prevalent system in North Europe and increasingly recommended post-COVID-19 for mitigating viral propagation. This paper presents a DBN setup with expert knowledge for an AHU with HRW, which is evaluated using experimental data from an office building in the Netherlands. The results show that the proposed DBN can successfully diagnose typical faults in HRW and HCV. @en

The 2050 decarbonization goals coupled with the growing housing shortage in Europe intensify the pressure on new-build dwellings to enhance their energy performance. Beyond a zero operational energy, the focus has shifted towards reducing embodied carbon (EC). Against this backdrop, this study investigates the simultaneous impact of downsizing and the use of timber in new-build dwellings, EC reduction strategies seldom explored concurrently. Through partial life cycle assessments, three scenarios are modelled: the Small, Medium, and Large House, with two construction variations for each, comparing a modular timber design to a conventional concrete alternative. Designs are based on dwellings built in Almere, the Netherlands. Data is extracted from the Swiss Ecoinvent database using the TOTEM tool and the static −1/+1 approach for biogenic carbon accounting is adopted. Results show a total EC ranging from 42,608 to 70,384 kgCO2eq for the timber designs versus 54,681 to 91,270 kgCO2eq for their concrete counterparts. Findings suggest that the relationship between house size and EC is sublinear whereby a house twice the size entails less than twice the EC emissions. Only the simultaneous implementation of downsizing and the use of timber achieved 53% carbon savings. The discussion explores implications of outcomes across academic, industry and policy perspectives, challenges in implementing smaller timber dwellings, and study limitations and future research. Beyond its empirical contribution, this paper offers a practical contribution with its hierarchical data analysis approach covering building, element and component. This approach can be implemented by researchers and practitioners alike to inform their design process.@en

The average energy consumption for heating of dwellings in the Netherlands has been decreasing over the last decades as a result of increasing thermal performance of new and renovated dwellings. However, this decrease is found to be lower than energy performance models predicted. One of the possible reasons is the behaviour of the residents, which is partially determined by the thermal comfort preferences of these residents. In this paper, the relationship between thermal comfort perception, indoor climate and energy consumption is investigated using high-resolution measurement data in 93 dwellings in the Netherlands. In the OPSCHALER project, data about thermal comfort perception, indoor climate and operational energy consumption were collected in 93 dwellings in the Netherlands during periods ranging from two to twelve months over a period of two years. Comfort perception was registered using the Comfort App, an application where users record their comfort data. Indoor climate data were collected per five minutes using sensors for temperature, relative humidity and CO2 concentration in the living room, kitchen and bedrooms of the dwellings. The Comfort App asked the residents for the room they are in, their comfort perception, their activities during the last half hour and the amount of clothing they wear. Significant relationships were found between thermal sensation and thermal preference, clothing level, metabolic activity level, activities related to thermal comfort taken in the last half hour, and indoor air temperature. These data can be used to compare the comfort level registered by the residents with the comfort level predicted by the PMV model, and link this to the indoor climate and the energy consumption for heating. This information can help to understand the relations between user preferences, indoor climate and energy consumption for heating.@en

The transition towards a Circular Economy (CE) in the built environment is vital to reduce environmental impacts, resource consumption and waste generation. The built environment can be made circular by replacing building components with more circular ones. There are many circular design options for building components and knowledge about which options perform better – from an environmental perspective – is limited. Existing guidelines focussed on single components, single circular design options, applied different assessment methods and provide conflicting guidelines. Therefore, in this article, we develop environmental design guidelines by comparing multiple circular design options for two building components: a kitchen (short service life) and renovation façade (medium service life). First, we synthesize design variants based on distinct circular pathways, such as renewable-, non-virgin material use, and modularity for reuse. Second, we compare their environmental performance to a ‘business-as-usual’ variant through Material Flow Analysis (MFA) and a multi-cycle Life Cycle Assessment (LCA) including extensive sensitivity analysis on circular parameters. Analysing the 78 LCAs and MFAs, we derive 8 lessons learned on the environmental design of circular building components. We compare our findings to existing guidelines, including those for circular building structures (long service life). Amongst other lessons, we found components with a short service life benefit more from prioritizing circular design options to slow and close future cycles, whilst components with a longer service life benefit more from reducing resources and slowing loops on site. However, applying circular design options does not always result in a better environmental performance. Tipping-points were identified based on the number of use cycles, lifespans and the assessment methods applied.

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Benchmarking is a method that can be used to measure progress and create awareness about the performance of organisations. Benchmarking the housing stock energy performance of Dutch housing associations can be used to measure and assess progress towards the decarbonisation of the housing stock. A new national climate agreement was signed in 2019, and in 2021 a new method to determine the theoretical energy performance of dwellings came into force in the Netherlands. To benchmark energy performance, a set of indicators is created that adequately represents the performance of Dutch housing associations according to the changed policies. A process involving key stakeholders is presented here to identify, assess and combine possible indicators. These were then integrated into four integrated models, which led to a final benchmark model. A model was chosen that consists of three indicators covering the energy performance of Dutch housing associations. The process and arguments that led to this final model are presented. While applicable within the Dutch context, the method and research results provide generalisable insights for the creation of energy performance benchmarks for building stocks.

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In practice, automated energy performance fault diagnosis systems are seldom installed in HVAC systems. The main reason is that a specific Fault Detection and Diagnosis (FDD) setup is time-consuming and expensive because the existing methods are component-specific, not aligned with HVAC design practices, and not fully automated. 4S3F (four symptoms three faults) method, based on system engineering and Diagnostic Bayesian Networks (DBN), was proposed to decrease the gap between the design of HVAC systems for buildings and energy performance diagnosis, and proofs of concepts were tested on diverse parts of the HVAC system of one specific building. In order to test the further applicability potential of the method, it is necessary to expand these tests and to study possible problems arising in practice, like the lack of sensors installed in a specific system or practical difficulties in the construction of the 4S3F Bayesian network by HVAC or control. However, due to the small number of validations carried out on the environment, parameters, and installation process of this method still need further discussion and refinements. In this paper, we investigate how to construct the DBN for the quite generic AHU (Air Handling Unit) of a, with mechanical supply and exhaust, heating and cooling coils, and heat recovery. The paper describes the possible DBN's depending on the technical design and the measurement points. The diverse Bayesians networks are compared, and it is concluded that also, with a limited number of sensors, a diagnostic network can be set up. It is also concluded that step-by-step instructions would be needed to facilitate the work of HVAC engineers when setting up the diagnosis model.@en

Achieving energy efficiency in the built environment requires extensive efforts in the renovation and adaptation of housing stock. A promising design solution is the heat pump. While gas boiler systems are commonly used in Dutch non-profit housing stock, the share of dwellings with a heat pump grew from 1.6% in 2017 to 3.2% in 2021. However, building characteristics and the energy consumption of dwellings with a heat pump are unclear. Therefore, a dataset of 69,422 dwellings with different types of heat pumps has been examined and compared to dwellings with a traditional HR107 condensing gas boiler. This research reports average characteristics and the average actual energy consumption of dwellings with all-electric, hybrid and gas absorption heat pump systems. Dwellings with a heat pump system are on average of higher building quality, their gas consumption is lower and their electricity consumption is higher than dwellings with an HR107 condensing gas boiler. Detailed insight is provided for dwellings with different heat pump systems and for dwellings with different building characteristics. Further research to determine the energy performance of dwellings with specific heat pump configurations is recommended in light of the energy transition in the built environment.

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The transition towards a Circular Economy (CE) in the built environment is vital to reduce resource consumption, emissions and waste generation. To support the development of circular building components, assessment metrics are needed. Previous work identified Life Cycle Assessment (LCA) as an important method to analyse the environmental performance in a CE context. However, questions arise about how to model and calculate circular buildings components. We develop an LCA model for circular building components in four steps. First, we elaborate on the CE principles and LCA standards to identify requirements and gaps. Second, we adapt LCA standards and propose the ‘Circular Economy Life Cycle Assessment’ (CE-LCA) model. Third, we test the model by assessing an exemplary building component: the Circular Kitchen (CIK). Finally, we evaluate the CE-LCA model with 44 experts. In the CE-LCA model, building components are considered as a composite of parts and materials with different and multiple use cycles; the system boundary is extended to include these cycles, dividing the impacts using a circular allocation approach. The case of the CIK shows that the CE-LCA model supports an ex-ante assessment of circular building components in theoretical context; it makes an important step to support the transition to a circular built environment.

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The Energy Performance of Buildings Directive (EPBD) enhanced the sustainable improvement of dwellings in the European Union. Member states formulated measurable goals to improve the housing stock, and monitoring systems were developed to give insights into the improvements. In the Netherlands, non-profit housing associations agreed to improve the quality of their housing stock to an average Dutch energy label B (energy index (EI NV) = 1.40) by 2020. Research assessing this progress over time is presented using an annual monitoring system based on 2.0 million energy performance calculations of 264 Dutch non-profit housing associations between 2017 and 2020. The assessment includes: a detailed description of the development of the state of the stock over time; the effect of changes to the stock (construction and demolition) and changes within the stock (different types of retrofit measures); and the different characteristics of non-profit housing associations. Insights from this research show which specific retrofit and other measures are adopted and have substantial impact over time. This provides a useful frame of reference for building stock analysis and accelerating the improvement of the building stock. It also creates a baseline of information for the future sustainable development of this particular stock.@en

In Europe, the energy performance of dwellings is measured using theoretical building energy models based on the Energy Performance of Buildings Directive (EPBD), which estimates the energy consumption of dwellings. However, literature shows large performance gaps between the theoretically predicted energy consumption and the actual energy consumption of dwellings. The goal of this paper is to investigate the extent to which empirical models provide more accurate estimations of actual energy consumption when compared to a theoretical building energy model, in order to estimate average actual energy savings of renovations. We used the Dutch non-profit housing stock to demonstrate the results. We examined three empirical models to predict the actual energy consumption of dwellings: a linear regression model, a non-linear regression model, and a machine learning model (GBM). This paper shows that these three models alleviate the performance gap by giving a good prediction of actual energy consumption on sectoral cross-sections. However, these models still have shortcomings when predicting the effects of specific renovation interventions, for example newly introduced heat pumps. The non-linear and machine learning model (GBM) outperform the theoretical model in terms of estimating energy savings through renovation interventions.

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Introduction. The building sector consumes 40% of resources globally, produces 40% of global waste and 33% of all emissions. The transition towards a Circular Economy (CE) in the built environment is vital to achieve Sustainable Development Goals (SDGs) such as responsible consumption and production. The built environment can gradually be made circular by replacing the current 'linear' building components with circular ones during maintenance and renovation. However, there are many possible design alternatives for circular building components; knowledge on which variants perform best - from an environmental perspective - is lacking. Methods. In this article, we develop environmental design guidelines for circular building components. First, we synthesize design variants for an exemplary circular building component: the Circular Kitchen (CIK). Second, we compare the environmental performance of these variants and a 'business-as-usual' variant by applying a Material Flow Analysis (MFA) and Life Cycle Assessment (LCA). Finally, from the results, we derive design guidelines. Results. We synthesized four design variants: (1) a kitchen made from bio-based, biodegradable materials, (2) a kitchen made from re-used materials, (3) a kitchen which optimises lifespans and materials, and (4) a modular kitchen in which components (with varying lifespans) are re-used by the manufacturer. From the LCA and MFA, we derived 7 design guidelines, which include: consider building components as a composite of sub-components, parts and materials with different and multiple use-, and life-cycles; match the materialisation of each part with the expected life cycle (merely substituting for re-used or low-impact materials does not provide the most circular design); facilitate various loops (e.g., repair, re-use, recycling) simultaneously. Conclusions. The presented design guidelines can support industry in developing circular building components and, through implementation of these components, support the creation of a circular built environment.

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Following regulation of the European Union, objectives were formulated to reduce energy consumption of the built environment in the Netherlands. For the stock of Dutch non-profit housing associations it was agreed to improve the average energy performance to an average energy index of 1.40 in 2020. This research assesses and gives insights in the progress to this objective for over 2.0 million dwellings of over 250 Dutch non-profit housing associations in 2017 and 2018. The assessment consists of an analysis of applied renovation measures, changes of the stock like new construction and demolishing, and clarifying characteristics of housing associations. It is concluded that large urban housing associations with adequate financial positions drive the improvement of the average sectoral energy performance. The improvement happens for a large part within the existing stock, mostly with traditional improvements like improved heating installations and improved insulation. Innovative solutions like: photovoltaic solar systems, combined heat and power systems, biomass systems, heat pumps and external heating, are responsible for a relative small part of the improvement within renovations. New construction and demolishing are also responsible for a relative small part of the annual improvement, but there is potential to improve this.

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Thermal renovations are considered to be an effective measure to reduce residential energy consumption. However, they often result in lower-than-expected energy savings. In this paper, we investigate some parameters that influence the probability on lower-than-expected energy savings. We do this by comparing actual pre- and post-renovation energy consumption of 90,000 houses in the Netherlands. The results of this study confirm that the effect of the parameters differ per renovation measure. For every renovation measure, the energy performance gap post renovation plays a significant role. This implies that the use of actual energy consumption data to determine the potential energy savings could therefore help to reduce the number of renovations resulting in lower-than-expected energy savings. Also, the energy efficiency state of the building pre-renovation plays an important role. One should take into account that renovations of energy inefficient buildings more frequently result in lower-than-expected energy savings than renovations of relatively energy efficient buildings. For the type of house we found that multifamily houses more often result in lower than expected savings when building installations are improved, while single-family houses renovations more frequently result in lower energy savings than expected when the building envelope insulation is improved. These insights can contribute to the decision making process whether or not to take a certain renovation measures, they can also help to manage expectations on housing stock level and individual building level.

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It is commonly accepted that occupants have a significant influence on the variation in residential heating consumption. However, the scale of that influence lacks empirical investigation. The aim of this study was to distinguish which part of the variance in actual residential heating consumption can be attributed to the occupants, and which part to the building itself. This was achieved by applying and extending a method suggested by Sonderegger in 1978, using updated and significantly improved data from two different countries: the Netherlands and Denmark. These data contain different types of heating supply systems (district heating and natural gas) and different housing forms (multi and single-family social housing, and private detached single-family houses). For the studied databases, the results indicate that approximately 50% of the variance in heating consumption between houses can be explained by differences related to occupants. The other 50% can be explained by the characteristics of the building itself and other physical parameters, which are often not taken into account in simulation models of heat transmission within buildings. Additional analyses indicate that the relative influence of occupants on heating consumption differs depending on the building characteristics of the dwelling. For example, the influence of occupants is larger when the building is more energy efficient. Based on the research results, it can be concluded that it is unrealistic to aim for a building simulation model that perfectly projects residential heating consumption for individual cases. However, creating building simulation models and occupant consumption profiles that accurately represent average residential heating consumption should be possible.

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Energy renovations often result in lower energy savings than expected. Therefore, in this study we investigate nearly 90,000 renovated dwellings in the Netherlands with pre and post renovation data of actual and calculated energy consumption. One of the main additions of this paper, compared to previous studies on thermal renovation, is that it only takes dwellings into account with the same occupants before and after renovation, using a large longitudinal dataset. Overall this paper shows new insights towards the influence of the energy efficiency state of a building prior to energy renovation, the type of building, the number of occupants, the income level of the occupants and the occupancy time on the actual energy savings, the energy saving gap and on the probability of lower energy savings than expected. We also investigate if the influence is different per type of thermal renovation measure. Some of the findings are: it is impossible to conclude which single thermal renovation measure is the most effective because this is dependent on the energy efficiency of the building prior to the energy renovation, type of building, income level and occupancy; occupants with a high income save more energy than occupants with low income; dwellings with employed occupants benefit more from improved building installations than dwellings occupied by unemployed occupants; The prebound and rebound effects are only part of the explanations for lower than expected energy savings; Deep renovations result more often in lower than expected energy savings than single renovation measures but nevertheless they result in the highest average energy saving compared to other thermal renovation measures. The results could be used for more realistic expectations of the energy reduction achieved by thermal renovations, which is important for (amongst others) policy makers, clients and contractors who make use of energy performance contracting, home owners, landlords and (social) housing associations and as a starting point to improve the energy calculation method.

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The Building Market Briefs reports profile a single country building sector condensed into 50 pages. The content addresses the opportunities, the interests of stakeholders, costs and benefits of action to mitigate emissions — the primary driver of climate change. Also, the content is amplified with recommendations from local experts. As the methodologically is aligned, comparison between different European markets is easily made.@en

The difference between actual and calculated energy is called the ‘energy-performance gap’. Possible explanations for this gap are construction mistakes, improper adjusting of equipment, excessive simplification in simulation models and occupant behaviour. Many researchers and governmental institutions think the occupant is the main cause of this gap. However, only limited evidence exists for this. Therefore, an analysis is presented of actual and theoretical energy consumption based on specific household types and building characteristics. Using a large dataset (1.4 million social housing households), the average actual and theoretical energy consumptions (gas and electricity) of different household types and characteristics (income level, type of income, number of occupants and their age) were compared for each energy label. Additionally, the 10% highest and lowest energy-consuming groups were analysed. The use of combinations of occupant characteristics instead of individual occupant characteristics provides new insights into the influence of the occupant on energy demand. For example, in contrast to previous studies, low-income households consume more gas per m² (space heating and hot water) than households with a high income for all types of housing. Furthermore, the performance gap is caused not only by the occupant but also by the assumed building characteristics.

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Sustainable development has been the focus of all major industries in the world, especially in the construction industry. As one of the sustainable construction modes, housing industrialization (HI) is now absorbing a growing number of attentions that lead the industry to go green. However, the implementation of HI in China is far from satisfactory due to its low economic efficiency. This paper attempts to improve the HI supply chain from a new perspective-transaction costs (TCs). First, it provides an objective understanding of status quo of HI in particular in China. Then, the study outlines the basis of TCs theories and supply chain management theory, compiling literature review of the application of TCs and supply chain management in other fields to states the feasibility of their application in HI area. A theoretical framework is developed to explain the relationships and overlaps among these three areas. Analysis of the state of research in application of TCs in HI supply chain management is expected to help optimized the governance structure of HI supply chain.@en

This report presents the results of the second part of the Monicair project1, which aim was to explore in how far the better determination of a number of parameters, which up to now were measured on-ly seldom, could support the development of better prediction models for the heating energy con-sumption in dwellings. Energy labelling calculations, as well as energy consumption forecasts, on which energy policies rely, are based on models. In the past years several studies have demonstrated that these models show large deviations from reality, making the prediction of possible energy sav-ings biased. These poor predictions can be hypothesised to be the result of poor estimation of the U–values of walls, poor estimation of the infiltration and ventilation flow rates and poor estimation of the heated surface area and of the temperature preferences of occupants. Additionally, there is very little knowledge on how occupant’s perception of comfort influences their ventilation and heating behaviour and finally the total energy use for heating. This report presents the results of a field study in which monitoring data was collected in order to further analyse parameters that could influence strongly the heating energy consumption and to fi-nally improve energy prediction models. A mix of modern and older dwellings was studied, as this can give a better idea of possible energy savings when renovating dwellings. @en