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.

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.

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.

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.

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.

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.

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.

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.