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Today's methods for data acquisition usually involve a considerable amount of manual work and, depending on the level of detail, must find a compromise between accuracy and workload. As a result, various typologies of building stocks have been developed. These typologies provide engineers and architects with rough descriptions for classifying buildings into certain periods and construction types. However, real-world applications lack objective decision-making, as these classifications are usually conducted by manual investigations of buildings. To overcome this subjective approach and to ensure the reproducibility of data acquisition, this paper proposes an image-based classification using Deep Learning (DL) methods. We present the acquisition of ground truth and describe the training and testing routines on a dataset of 5,307 images obtained in the city of Weimar (located in the Free State of Thuringia, Germany), a mid-sized city representative of eastern Germany. All building images are linked to an existing building typology, allowing a classification of buildings in ten predefined and acknowledged periods. These periods are ranging from pre-1859 to post-2002. As a benchmark, an expert human baseline is acquired, showing a lower prediction accuracy than the results obtained by DL methods. Our results highlight the potential for automation in image analysis and beneficial impacts for Building Energy Modeling (BEM) on urban scale. Thus, a trustworthy data acquisition regarding the building age holds benefits for various stakeholders in BEM (engineers, architects, policymakers as well as customers). • Two CNN-architectures (ResNet50 and AlexNet) are used for image-classification. • Extraction of annotated image-patches facilitates an enhancement of the image set. • Quantity of training images and applied optimizer significantly influence results. • CNN-based estimations successfully linked to existing and popular building typology. • Accuracies of CNN-based estimation of building age outperform the human baseline. [ABSTRACT FROM AUTHOR]

In Germany, policy-induced energy efficiency improvements typically aim at reducing primary energy consumption. Private households, on the contrary, pursue the maximization of wellbeing or, in microeconomics jargon, the maximization of utility of the occupants. There is a marked difference between upfront-calculated energy performance ratings (EPRs) and actual heat energy consumption (HEC). From an energy and environmental policy point of view, deviations of energy consumption from ex ante calculated EPRs is problematic, as it jeopardizes their usefulness for (prospective) homeowners, policy-makers and researchers relying on EPRs as a guidance. The EPR-HEC gaps reported are, apart from deviations from the mean aggregate values (i.e. heterogeneity), often attributed to anticipated or unanticipated behavioral effects. From an energy economist's point of view, energy rebound induced by lowered costs per unit of energy service due to increased energy efficiency is one explanation. The related literature almost entirely treats building-specific EPRs as universal standards, trying to explain the empirically observed discrepancies. In this paper, we investigate to what extent the current EPR scheme in place in Germany today can address behavioral issues. To this end, based on two different multi-family housing data sets for Germany and regression analysis, we empirically investigate the deviations between EPRs used in the regulatory norms and the HEC levels observed in reality. We find evidence that it is not necessarily the behavioral dimension, but rather the static and mostly technically guided calculation of the EPRs itself that accounts for the major part of the deviations. We conclude that there is indeed a need for further improvements in the field of EPR regulation and methodology. [ABSTRACT FROM AUTHOR]

The Solar Decathlon is a competition for universities from all over the world which focuses on designing, building and operating experimental, solar-powered houses. Participating in the project offers universities a unique and interdisciplinary platform for teaching, learning and research which combines practical experience with research. In 2022, the European edition was held in Germany for the first time. The event took place with a new urban profile to increase both the relevance of the competition and the learning experience. Its main topic was the further development of the European city, and specifically focused on the existing stock of residential apartment buildings. A total of 18 teams from 11 countries with over 500 students took part, with 16 teams ultimately building their houses on a shared solar campus. Demonstrating a balanced or a positive energy balance in practice was one of the essential goals of the competition. This was achieved by 13 of 15 projects in the energy contest. The prerequisites for this were a high level of energy efficiency and the consistent use of solar energy. Both strategies were embedded in convincing architectural concepts. These ranged from the minimised visibility of standard systems on rooftops, through to custom-built systems with full architectural integration in façades. Hybrid solar systems also became a focus, with the goal of making optimum use of the surfaces on the building envelope. This paper focuses on the energy engineering and technical and architectural integration of the solar systems. It also includes the results achieved in the competition linked to the learning experience. [ABSTRACT FROM AUTHOR]

In the planning phase of district energy systems, optimization models based on mathematical programming are a widely used approach. Most optimization models determine the optimal energy system design based on a single representative year. However, this approach is unable to consider changing economic and technological boundary conditions over the system's lifetime. As a result, these one-period models fall short for districts with 5th generation district heating and cooling (5GDHC) networks, which are typically developed over long time periods and are built in multiple construction phases. In this paper, we present two multi-period optimization approaches for designing 5GDHC districts: The first approach is a forward-looking model which determines the optimal investment pathway by using a perfect foresight of future parameter developments. In the second approach, a one-period model is solved repeatedly for every investment period without knowledge of future parameter developments. Both approaches are compared to a one-period model for a real-world 5GDHC district in Germany. In comparison with a one-period design approach, the forward-looking method leads to total cost savings of up to 17 % and the sequential method of up to 11 %. By using a forward-looking model, gas-fired technologies are sized smaller while the capacity of electricity-driven technologies in the energy hub as well as photovoltaic modules and thermal energy storages in buildings increases compared to a one-period model. The case study shows that a multi-period modeling approach is an important addition to design optimization models for 5GDHC networks and can have a significant impact on the optimal design. [ABSTRACT FROM AUTHOR]

The increasing need for energy conservation has led to the development of a range of energy models for assessing energy demand in the residential sector of a country. Even though such models deliver a principal solution for forecasting energy demand and assessing the impact of future energy saving measures, collecting the required baseline data is fraught with difficulties such as a complete lack of data, missing data within a dataset and a lack in coherence between different datasets in terms of detail, data collection method, baseline assumptions and sample size. This paper analyses the transferability and accuracy of twelve energy models (MAED-2, FfE-Gebäudemodell, CDEM, REM, CREEM, ECCABS, REEPS, BREHOMES, LEAP, DECM, CHM, BSM), taking Germany as case study example. Furthermore, a sensitivity analysis is conducted for each model to analyze the significance of the input variables for the overall modelling outcome, highlighting the most influential variables. It is shown that models with a high level of disaggregation do not necessarily guarantee more accurate results. Adjustments are proposed to improve the transferability of the models to the case study country Germany. [ABSTRACT FROM AUTHOR]

Zero Energy Buildings (ZEBs) are considered as one of the key elements to meet the Energy Strategy of the European Union. This paper investigates cost-optimal solutions for the energy system design in a ZEB and the subsequent grid impact. We use a Mixed Integer Linear (MILP) optimisation model that simultaneously optimises the building’s energy system design and the hourly operation. As a ZEB have onsite energy generation to compensate for the energy consumption, it is both importing and exporting electricity. The hourly time resolution identifies the factors that influence this import/export situation, also known as the building’s grid impact. An extensive case study of a multi-family house in Germany is performed. The findings show that the energy system design and the grid impact greatly depend on the ZEB definition, the existing policy instruments and on the current energy market conditions. The results indicate that due to the feed-in-tariff for PV, the cost-optimal energy design is fossil fuelled CHP combined with a large PV capacity, which causes large grid impacts. Further, we find that heat pumps are not a cost-optimal choice, even with lower electricity prices or with increased renewables in the electric power system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Simulation of residential building energy supply with heat pump or hydrogen systems. • 2050: Hydrogen economically-viable if H 2 -/Elec.-price is less than 34–61 % per kWh. • For conservative price predictions: Electric heat pumps are most economical solution. • Energy carrier switch for single-family houses: H 2 -/Elec.-price below 44 % per kWh. • Energy carrier switch for multi-family houses: H 2 -/Elec.-price below 58 % per kWh. • Fuel cells are emerging as an economically-viable option for multi-family houses. Fossil-fueled heating in the building sector is responsible for 16 % of Germany's total CO 2 emissions. Options for the climate-neutral heating of buildings include renewable electricity or renewable hydrogen. In this paper, we conduct a bottom-up study to investigate the role of hydrogen in the climate-neutral energy supply of ten selected residential buildings in comparison to electricity-based systems. Based on a demand simulation and linear optimization of the supply system, sensitivity analyses identify the threshold values of the hydrogen price for the use of hydrogen technologies (hydrogen boilers or fuel cells) in building energy systems and make the quantities of hydrogen consumed visible. The results specify the first installations of hydrogen-operated fuel cells in multi-family houses if the hydrogen price reaches 0.17 €/kWh H2 in 2050 at an electricity price of 0.31 €/kWh el. Generally, for different building types and electricity supply prices, it indicates that the use of hydrogen becomes economically-viable if the price of hydrogen supply is in the range of 34–61 % of the price of the electricity supply per kWh and below. For the case of green hydrogen obtained using renewable electricity, it is highly questionable whether hydrogen supply will be that much cheaper than direct electricity supply, making its economical use in buildings uncertain. [ABSTRACT FROM AUTHOR]

Passive Houses are buildings which provide comfortable indoor conditions at an extremely low heating and cooling load. The peak daily average heating and cooling loads are typically below 10 W/m 2 and annual useful energy demands are below 15 kW h/(m 2 a). The Passive House standard was originally developed in Germany. In this paper we show by hygro-thermal dynamic simulation that it is possible to realize residential Passive Houses in all of the world's relevant climate zones, represented here by Yekaterinburg, Tokyo, Shanghai, Las Vegas, Abu Dhabi, and Singapore. The window quality, insulation levels, and mechanical services all depend on the climate as well as on the building's shape and orientation. The resulting annual energy demand for space conditioning of the Passive Houses is 75 to 95% lower than that of a traditionally insulated building of the same geometry. In humid climates like Shanghai or Singapore, special attention must be paid to humidity aspects. In climates which are hot and humid all year long, the total useful energy demand for sensible and latent cooling may exceed 70 kW h/(m 2 a) even in a Passive House. Finally, it is shown that the architectural quality is not compromised by the Passive House requirements. [ABSTRACT FROM AUTHOR]

• Linking top-down energy efficiency indicator analysis with bottom-up policy analysis. • Explaining the contribution of energy efficiency policies in Germany and Switzerland. • Analysing the counteracting effects to energy policy of rebound and non-compliance. This paper studies the factors underlying the evolution of the energy consumption for space heating in residential buildings by linking top-down analysis, based on meso -indicators, and bottom-up analysis, based on policy evaluations. The top-down analysis (i.e. the perspective from energy statistics) allows one to separate the change in total energy use into activity level, societal factors and energy efficiency gains. The explanatory power of the resulting meso-(statistical) indicators is often limited, if the underlying factors are not examined (e.g. changes in heating levels and patterns, weather effects, cost of energy and policies regarding insulation and heating system standards). We overcome most of these drawbacks by conducting a bottom-up analysis (i.e the perspective from single policy measures), which enables us to discern the contribution of energy efficiency policies to the changes observed with the meso -analysis. We focus on space heating consumption in the residential sectors for Germany and Switzerland. A major aim of this analysis is to show the contribution of energy efficiency policies (such as thermal building regulation, subsidy programmes, fiscal measures etc.) towards the changes in this indicator. The results show that the progress in energy efficiency (both autonomous and policy induced) in both countries had the greatest effect (-776 PJ for Germany, −42 PJ for Switzerland) regarding the change in energy consumption for space heating in the period from 2000 to 2016. However, the impacts of "technical and comfort" rebounds (+436 PJ for Germany, N/A for Switzerland) and other developments such as societal changes (+316 PJ for Germany, +35.5 PJ for Switzerland) were found to compensate for a significant part of the energy efficiency gains. In both countries, it was possible to link physical energy efficiency indicators to policy evaluation, but limitations were also identified which are primarily related to data gaps. [ABSTRACT FROM AUTHOR]

• A new retrofitting strategy by using prefabricated elements window machine. • Minimization of the renovation costs and speeding up the retrofit action. • A potential in energy saving and improving the Indoor Environment Quality. • Further significance in retrofit and healthy home office working environments. The renovation of existing residential buildings across Europe has become a key factor for energy saving and the improvement of Indoor Environment Quality (IEQ). This paper presents an investigation of IEQ for a typical 1960s single-family house in Germany before and after a new retrofitting strategy (prefabricated element with integrated ventilation and new thermal envelope). In-situ measurements were applied to investigate the energy efficiency and IEQ improvement for the new retrofitting strategy in temperate winter regions of Europe. The simulation model replicates the measured data to be closer to reality and simulates a home office (COVID-19) scenario. The in-situ measurements showed that our retrofitting strategy increases the indoor air temperature during the heating period by up to 2.5 °C, decreases relative humidity by up to 3.8% and increases CO 2 by up to 24 ppm than before. As the simulation results shows, the retrofitted home office scenario allows a reduction in heating energy of up to 77%, an increase in CO 2 concentration of up to 41.8 ppm, 0.5% less relative humidity and 1.6 °C higher indoor air temperature than that without retrofitting. The new approach to retrofit residential buildings thus showed a potential in energy saving and improving the IEQ. [ABSTRACT FROM AUTHOR]

• Grouping techniques are used to select representative buildings for stock modelling. • Three grouping approaches are applied to a case study from Germany. • Partition comparison relies on validation indices and impact on demand modelling. • Grouping techniques & selection indices impact the representative buildings chosen. Grouping techniques are frequently used in urban studies as a means for partitioning building stock heterogeneity. The choice of grouping techniques and selection indices can have a significant influence on the segmentation of building stock and associated identification of representative buildings. To date, this issue has not been systematically examined nor justified. The current paper compares different grouping and partition selection techniques using residential building data from Germany. Three grouping techniques are investigated: supervised, unsupervised and semi-supervised. The unsupervised approach is addressed through three clustering algorithms in this work: agnes, diana and partition around medoids. The semi-supervised approach consists of typology-based seed buildings coupled with a distance-based grouping, while the supervised approach relies on classification rules. The resulting partitions are assessed through multiple criteria: internal indices (CH, Dunn2, Silhouette), external index (F-measure) and impact on heating demand modelling (Heating Demand Error). Results show that the algorithms and the selection indices impact the choice of representative buildings to be modelled. Moreover, considering the F-measure, similarities between the three techniques results were observed on some of the groups. Parameters to account for when selecting a grouping technique are discussed and include the number of groups, group uniformity, and compactness/separation. [ABSTRACT FROM AUTHOR]

• A novel metric (DOC) for bidirectional low temperature networks (BLTNs). • DOC describes the balancing potential of heating and cooling demands in a district. • DOC helps to identify building clusters with complementary demand profiles. • Correlations between DOC and KPIs are elaborated based on 63 demand scenarios. • Performance comparison of BLTNs with state-of-the-art building energy systems. Bidirectional low temperature networks (BLTNs) are a promising technology to drive decarbonization of the heating and cooling sector. The energy efficiency of a BLTN strongly depends on the heating and cooling demands of the connected buildings and their simultaneity. In order to evaluate the efficiency of a BLTN, a novel metric, called Demand Overlap Coefficient (DOC), is presented in this paper. The DOC can be calculated for individual buildings or a district and describes which proportion of heating and cooling demands can be balanced inside buildings and between buildings. The DOC is calculated with time series for heating and cooling demands, taking special account of their simultaneity. For a real-world use case in Germany, it is shown that 25% of heating and cooling demands can be balanced in buildings and 45% can be balanced by the BLTN between buildings. The DOC is evaluated for 63 demand scenarios. Correlations show that for districts with a DOC larger than 0.45, BLTNs have lower annualized costs than a state-of-the-art heating and cooling system. BLTNs have higher exergy efficiencies for districts with DOCs larger than 0.3. With the derived correlations, the DOC serves as a practical metric in the planning process of BLTNs in order to identify and assemble building clusters with complementary demand profiles. [ABSTRACT FROM AUTHOR]

• Stochastic bottom-up model for flexibility considering user-behaviour, sizing and controls. • Flexibility modelled as electric storage equivalent. • PV, CHP, EV, HP, Batteries and appliances considered. • PV, EV most potential on individual base, HP most potential with respect to number of units. This paper presents a stochastic bottom-up model for flexibility in the residential electric load profile. The model accounts for user-behaviour and all relevant technologies, characterising their diversity in sizing and controller settings. The flexibility of technology is mapped into a linear electrical storage equivalent model, which is parameterised on-the-fly during simulation of the load profiles. Using a linear storage representation makes it suitable for optimisation methods that are commonly used in optimal controls. The model is used to study the flexibility of individual technologies as well as of a residential area in Germany taking into account technology penetration rates. The results indicate that EV and PV battery systems offer the largest values of switchable power and storage capacity. However when technology penetration rates are taken into account, heat pumps emerge as a major provider of flexibility. [ABSTRACT FROM AUTHOR]