The energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.@en

After the thermal renovation of a dwelling, there exists a gap between the actual and predicted energy performance. One of the reasons contributing to this gap is the poor assumptions of building thermal characteristics during the prediction stage. Nowadays, smart meters for gas and electricity, and home automation systems are becoming increasingly prominent in dwellings. Hence, there is potential to use the on-board monitored data from these sources to estimate the thermal characteristics of the actual dwellings. If it was possible to measure everything in a dwelling, then the estimation of these characteristics would become easy. However, the amount of data from the dwellings is limited. Hence with the available data, assumptions have to be made to estimate characteristics reflective of the actual dwelling. Therefore, this study investigates the impact these assumptions have on the estimated characteristics. First, a simple equation requiring minimum data is formulated to represent the heat dynamics in a building. Then, the characteristics are determined for one Dutch dwelling for the following conditions: 1. Different measurement periods, 2. Different time granularities, 3. With total (space heating + domestic hot water) and decomposed (only space heating) gas consumption data, 4. With different representations of indoor air temperature, and 5. Using electricity data to account for internal heat gains. In general, the estimated characteristics deviated for all the conditions. And thus, this study establishes the importance of well-chosen on-board monitored data.

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Buildings need to be carefully operated and maintained for optimum health, comfort, energy performance, and utility costs. The increasing use of Machine Learning combined with Big Data in the building services sector has shown the potential to bring energy efficiency and cost-effectiveness. Therefore, upskilling and reskilling the current workforce is required to realize new possibilities. In addition, sharing and preserving knowledge are also required for the sustainable growth of professionals and companies. This formed the basis for the Dutch Research Council funded TransAct project. To increase access to education on the job, online learning is experiencing phenomenal growth. A study was conducted with two focus groups - professionals of a building service company and university researchers - to understand the existing challenges and the ways to improve knowledge sharing and upskilling through learning on the job. This study introduced an Enterprise Social Network platform that connects members and may facilitate knowledge sharing. As a community forum, Yammer from office 365 was used. For hosting project files, a SharePoint page was created. For online courses, the company’s online learning site was utilized. The log data from the online tools were analysed, semi-structured interviews and webinars were conducted and feedback was collected with google forms. Incentive models like social recognition and innovative project results were used to motivate the professionals for online activities. This paper distinguishes the impacts of initiatives on the behaviour of university researchers vs company employees.@en

The HVAC sector is essential to realize the energy transition and is facing numerous challenges like educating enough HVAC engineers to carry out the task and being able to integrate knowledge from the construction, energy, IT and health sectors and to cope with rapid technological changes. The availability of structured and easy-to-follow courses on HVAC and energy systems for buildings at higher education level could help to motivate (future) engineers to contribute to the HVAC sector, and to understand how challenging and high-tech it is. Such a course program would ideally also bring a basic understanding of the field to architects and building engineers, in such a way that a better common ground is created for collaboration and integrated design. It would also be useful to Machine Learning and Artificial Intelligence experts joining the HVAC sector. Last but not least, it could help bridging the gap between engineering and policy making, by here too, offering common views on primary energy, resource depletion and CO2 emissions relating to HVAC systems. The paper describes the structure and content of such an on-line course program. It was developed based on years of teaching experience with international master students of Mechanical Engineering, Civil Engineering, Architecture, Technical Management and Policy, Electrical Engineering and with professionals from housing associations, ministries and municipalities. The choices for the program structure, based on systems engineering, are underpinned and explained, as well as the choices for specific contents. Additionally, experience with the development of self-assessment tools for students, and self-paced courses is shared, as well as the feed-back from students. A first version of the course program was tested on the edX platform with more than 5000 students participating in each module and is publicly available.@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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Low Temperature Heating (LTH) of buildings is a key feature when switching to renewable energy. Even when the capacity of LTH is high enough, LTH may adversely affect indoor thermal comfort in case buildings are not suitably insulated. This paper goes deeper into methodological issues when conducting a thermal comfort assessment. Thermal comfort is either quantified by Fanger’s Predicted Mean Vote (PMV) or ranked in building comfort classes in the adaptive model. In both cases, one of the main parameters influencing comfort is the Mean Radiant Temperature (MRT). This study addresses issues with common MRT and PMV calculations in energy simulation software. The case study is TRNSYS 17. Several MRT and PMV calculation methods are compared, showing possible draw-backs and deviations from comfort standards NEN-EN ISO 7726 and 7730. For instance, in the standard heating settings in TRNBuild only the total heating capacity is specified. The radiative part is then distributed area-weighted over opaque surfaces. A more detailed option in TRNBuild is to specify the locations of radiative gains as points. In both cases, the MRT at a comfort sphere is calculated with Gebhardt-factors instead of view-factors. The standard settings may be considered too simplified for detailed comfort studies whereas the detailed model shows deviations from comfort standards NEN-EN ISO 7726 and 7730. Therefore, two additions to these models are proposed to increase accuracy. One addition is an ordinary detailed model with radiative gains as point sources in order to retrieve all surface temperatures during a desired period of time. In the second addition walls with radiators are split-up and planes are added at the locations of radiators to generate a view-factor matrix. This can be done in TRNBuild, but also in other view-factor calculation software. From model 1 all surface temperatures are retrieved. Combined with the view-factors from model 2, the MRT can be calculated.@en

Many researchers have indicated the energy performance gap (difference between actual and predicted energy used in buildings), not only on an individual building level, but also on a building stock level. For policy makers it is important that predictions are correct on an building stock level to make them a useful tool to predict the effect of their proposed energy saving policies. Often not all input parameters for building energy simulations are known (e.g. insulation rates are often only possible to determine with destructive inspection or extensive measurements), therefore assumptions are made (e.g. assumptions for insulation rates are often made based on construction year). It is expected that a large part of the energy performance gap on building stock level are caused by incorrect assumptions of the unknown parameters in the building simulations. Previous research has shown that automated calibration of the assumptions on building stock level seems a promising method to reduce the energy performance gap and therewith make building energy simulations on building stock level a more reliable tool for policy makers. The previous research about calibration on building stock level was a proof of concept and still needs some improvements before it can be applied in practice. One of the aspects to improve the method is to determine the most suitable objective function and the most suitable optimization algorithm. In this paper we compare different objective functions (e.g. Root Mean Square Error, Mean Absolute Error, Sum of Absolute Errors). Next to that we compare different optimization algorithms (e.g. Genetic Algorithm, Particle Swarm and simulated Annealing Algorithm). For the comparison of the objective functions and the algorithms the former Dutch calculation method to determine the energy label in dwellings is used, in combination with the SHAERE database and data from the Dutch Statistics. The SHAERE database contains all input information on individual dwelling level to calculate the energy label of a dwelling of almost 2 million dwellings. The Dutch Statistics database contains the individual annual energy use of all dwelling of the Netherlands and can be linked to the SHAERE database.@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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Reduction of energy consumption is currently high on the political agenda of many countries. Because buildings consume a significant amount of the total energy consumption they form a big energy saving potential. For this reason the EPBD was introduced. This directive introduced a mandatory energy performance certificate for all buildings in Europe (in the Netherlands implemented as energy label). The initial aim of this directive was to make people aware of the energy efficiency state of the building that they buy or rent.@en

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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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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The housing stock has a major share in energy consumption and CO2 emissions in the Netherlands. CO2 emissions increased 2.5% year-on-year in the first quarter of 2018. Higher CO2 emissions were principally due to raised gas consumption for heating in the residential and service sector¹. Energy efficiency renovations can contribute considerably in reducing energy consumption and achieving the EU and national energy efficiency targets. However, based on recent research², the renovation rates in the Dutch social housing sector are not adequate to achieve the energy efficiency targets. Moreover, the deep renovation rates are almost negligible in this sector. The Dutch housing stock consists of the owner-occupied sector and rental sector (social housing and private rental houses) with shares equal to 69.4% and 30.6%, respectively. Considering the major share of the housing sector in energy consumption, the aim of the current study is to evaluate and compare the renovation rates in these sectors and the potential contribution of each one in achieving the energy efficiency targets. By renovation rate, we mean the percentage changes in the number of the identical houses moving from one energy label to the more efficient energy labels. The Netherlands Enterprise Agency (RVO) and Statistics Netherlands (CBS) databases are used to conduct the statistical analysis. The results show that the renovation rates are almost the same in these three sectors, despite the expectation of much higher renovation rates in the social housing sector.

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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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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 Paris Agreement on climate change implicates huge improvements of the energy performance of buildings and the energy infrastructure. The European Union formulated high ambitions for the built environment. The existing housing stock is responsible for a major share of energy use and is considered to have a high potential to contribute to the savings. Therefore, the Netherlands aims for a carbon neutral housing stock in 2050. The consequences of this goal and the ways of how to realise it are yet unclear. Most emphasis in policies and practices is on reducing the energy demand for heating by renovation of the existing stock and bringing the dwellings to a higher energy performance standard. The targets, policies and renovation programmes are already applied for several years now and the question arises of what progress can be seen in renovation activities and energy saving results. This paper is based on data on improvement rates of the Dutch non-profit housing sector and insight in the relation with actual energy reduction. Based on these insights the challenges for 2050 are discussed. @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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The energy saving policies of governments are not resulting in the energy saving they were aiming for[1]. Theoretical energy saving predictions are an important and frequently used tool for policy makers to develop energy saving policies and to set energy saving targets. Majcen et al. [2] showed a discrepancy between actual and theoretical energy consumption. The existence of the energy performance gap means that policy makers base their policies on assumptions that are not always right. It is expected that a significant part of the performance gap can be explained by the occupant behaviour, therefore a better insight in the influence of occupant behaviour on actual residential energy consumption is required. In this paper a method is introduced to analyse the influence of occupant behaviour on residential energy consumption, based on the principle that if occupant behaviour is studied also the building characteristics should be taken into account. In this paper a data analysis is executed with the use of a large building characteristics database (SHAERE - over 2 million cases), occupant data (Statistics Netherlands - entire Dutch population) and energy data (Statistics Netherlands – entire Dutch population). Although the results of the executed analysis are not conclusive, several important factors were found for further research. Firstly, the building and occupant cluster variables should be created with great care since they are one of the determining factors for correct function of the method. Secondly the quality of the dataset is of major importance for the final result. Finally, for further research it is advised to execute this method on other datasets, and compare the results in order to define which aspects are most important for applying this method.@en