Renovation Scenarios Research Articles

PredicTAIL, a prediction method for indoor environmental quality in buildings undergoing deep energy renovation based on the TAIL rating scheme

The recently developed TAIL rating scheme enables assessment of the changes in the indoor environmental quality (IEQ) associated with a building’s deep energy renovation (DER) and classification of the resulting quality levels of the thermal (T), acoustic (A), and luminous (visual) (L) environments and indoor air quality (I). Since the TAIL rating is primarily based on measurements, it cannot be determined prior to renovation operations to help design the IEQ. To fill this gap, the PredicTAIL method was developed in the present study to predict the changes in ten of the twelve TAIL parameters as a result of DER. These parameters are indoor air temperature, relative humidity, sound pressure level, daylight factor, illuminance, and concentrations of carbon dioxide, formaldehyde, benzene, radon, and PM2.5; no prediction is made for ventilation rate or mold. To examine the feasibility of the PredicTAIL method and the sensitivity of the existing models for quantifying changes in the TAIL parameters corresponding to different renovation strategies, simulations were performed in a hotel and an office building using TRNSYS, IDA ICE, ACOUBAT, MATHIS-QAI, and PHANIE. These modeling tools were first benchmarked against the TAIL parameters measured in the buildings before renovation. Once the agreement between measurements and modeling was considered acceptable, four pragmatic renovation scenarios were applied, and their impact on the IEQ parameters was quantitatively modeled. The simulations showed that the quality levels of the IEQ parameters were improved or unchanged for some parameters but degraded for other parameters after DER. The changes in the IEQ parameters and the TAIL rating depended on the renovation scenarios, suggesting that the PredicTAIL method is sufficiently sensitive to guide renovation design.

A combined principal component analysis and clustering approach for exploring enormous renovation design spaces

This paper presents a new approach for supporting renovation designers in exploring the vast landscape of renovation scenarios. The approach integrates renovation scenario sampling (based on a semantic domain model of renovation, called NovaDM), scenario clustering (specifically k-means) in terms of Key Performance Indicator evaluations, and dimensionality reduction (specifically Principal Component Analysis) so that the sampled scenarios and discovered clusters can be visualized in a meaningful way. The approach is empirically evaluated on a large residential building case study in Denmark. Case study results show that the visualization captures and communicates critical high-level information about the "structure" of the enormous, complex underlying renovation design space, in terms of qualitative KPI profiles (e.g. fairly low privacy, very high cost, etc.), thus significantly enhancing renovation decision making.

Potential of Energy Savings in the Public Housing Stock of Comunitat Valenciana Region by Applying the MedZEB Cost-Optimal Methodology

Improving energy efficiency in buildings has a key role to play in achieving the ambitious goal of EU-wide climate neutrality by 2050, set out in the European Green Deal. This paper describes a cost-optimal analysis of residential buildings of Valencian Community, Spain. Thus, an assessment of the contribution of total primary energy savings per year (MW/h) of the social dwellings managed by EVha, Entitat Valenciana d’Habitatge i Sòl (eng. Valencian entity for dwelling and ground) towards the national contribution is presented in this paper. To assess it, the MedZEB cost-optimal methodology has been applied to optimise the performance of the building’s envelope. This means that Optimal Renovation Strategies through Life-Cycle Analysis have been applied to obtain the Packages of Optimal Solutions of the different reference buildings in a reference climate. First, the renovation scenario with 100% of the building stock being renovated has been calculated. Then, the renovation scenario of 1%, being the current European rate of renovation and, finally the renovation scenario of 2%, given that the objective of the Renovation Wave is to at least double the annual energy renovation rate of residential and non-residential buildings by 2030 and to foster deep energy renovations.

A novel LCSA-Machine learning based optimization model for sustainable building design-A case study of energy storage systems

Life Cycle Sustainability Assessment (LCSA) in the built environment has increasingly drawn researchers’ attention in the preceding years; however, the lack of an integrated LCSA based model is still a barrier for its effective implementation into the building design process.This paper proposes a new comprehensive LCSA model to be integrated into the design process of new buildings and energy refurbishment scenarios of existing ones. The proposed model consists of sets of mathematic equations to describe LCSA pillars of buildings, including Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (SLCA) as the intermediate indices. A final LCSA index is then provided to describe a design scenario's performance from an LCSA perspective using the authors' new formulation and weighting method. The model is also integrated into the optimization process and enhanced with Machine Learning (ML) methods to accelerate the design-assessment process while preserving its accuracy. Finally, given the transition towards buildings' electrification by Renewable Energy Sources (RESs), as a supportive technology, the size-optimization of a residential building's short-term thermal and electrical Energy Storage Systems (ESSs) is chosen to demonstrate the model's capabilities. For this purpose, the building case study is parametrically modeled in Grasshopper, Energy plus, and Matlab for energy analysis, data processing, and machine learning.

An analysis of emission reduction strategy for light and heavy-duty vehicles pollutions in high spatial–temporal resolution and emission

Traffic as the main source of urban air pollution created severe problems for human health and sustainability. To propose a bottom-up method in emissions reduction, accurate emission inventories of vehicles in a medium-sized city are developed. Traffic emission factors were obtained using traffic flow data, traffic control cameras, and International Vehicle Emission (IVE) model to calculate CO, VOCs, NOx, SOx, and PM of passenger cars, taxis, and urban buses emission inventory. Annual overall emissions of CO, VOCs, NOx, SOx, and PM pollutants, respectively, are 346, 20.9, 25, 44.4, and 0.5 kt/year. VOC and CO emissions in the start-up phase are in the scale of the running phase, while NOx, SOx, and PM allocate much less than the running phase. The highest emission value of SOx and PM occurs in arterials while CO, VOC, and NOx in highways. Eight renovation scenarios have been designed to evaluate their environmental and economic efficiency. Two scenarios entitled "renovation of carbureted, Euro 1 and Euro 2 LDV" and "renovation of high mileage Euro 1 and Euro 2 standard urban buses" showed the highest decrease in pollution emission and pollution social costs. For these two important scenarios, implementation costs were calculated at 477 and 46M$, while social costs decrease are calculated to be 172.6 and 77.5M$, respectively. Renovation of vehicles could benefit both the government and society by reducing fuel consumption and pollutant emission. The emission mitigation scenarios considering mobile sources could be a guide for adopting policies in developing countries. Governmental and social cost-share and governmental and social repayment because of fuel-saving costs have also been calculated.

Energy analysis of Texas Metropolitan Areas for climate change mitigation using LiDar

Buildings account for a large share of U.S. energy use making the building sector a prime candidate for efficiency measures. Obtaining accurate information about geometry and occupancy can prove computationally intense. We analyze different neighborhoods in Texas under a new methodology that allows for large scale implementation. This methodology uses PostgreSQL, QGIS, LiDar, Grasshopper and CitySim to generate, analyze and compare energy models under various climate change scenarios. An energy analysis was performed on over 100,000 buildings in Dallas, Texas, evaluating performance in terms of energy efficiency and overheating under several refurbishment scenarios. Neighborhoods in Dallas were analyzed under a modified methodology designed to scale the results of a previous, smaller study by the authors to a broader sample. This study found metropolitan areas performed similarly under various refurbishment scenarios; the more aggressive scenarios showing greater improvements for the same given amount of time. The results of the experiments show consistency between this expanded method and the original. This method must make concessions in terms of detail. While it is possible to achieve a more accurate estimate by taking into account variables such as enclosure composition, building systems and shading surfaces, it becomes difficult for larger simulation, making LiDar an effective alternative.

Energy and Aesthetic Upgrading Interventions: Assessing Urban Block Renovation Scenarios

The target towards carbon neutral cities emerges as a new way to mitigate the environmental pollution and the indisputable climate change, but it can also be the chance for the overall improvement of the quality of life by promoting the upgrading of the often low aesthetic quality of the urban environments. The objective of the research is to investigate renovation strategies at the city scale that will not only reduce the energy demand, but they will also promote the improvement of the city’s aesthetics. Two different typologies of urban block forms of the Greek city are examined, and four scenarios of renovation interventions are analysed according to energy and aesthetic criteria. The heating and cooling load demand is calculated through simulations for the four renovation scenarios which include: The improvement of the thermal insulation of the building’s shells. The use of phase change materials. The construction of green roofs. The integration of passive solar and shading systems. The findings of the research prove that the energy interventions at the urban block scale can define the degree of efficiency in the energy performance level without compromising aesthetics, as the added value resulting from higher aesthetics is a basic criterion for the overall improvement of the quality of life.

Weather-induced variability of country-scale space heating demand under different refurbishment scenarios for residential buildings

The decarbonisation of residential heat through integration with the power system and deployment of refurbishment policies is at the core of European energy policies. Yet, heat-electricity integration may be challenged, in practice, by the large variability of heat demand across weather years. Current approaches for residential heat demand simulation fail to provide insights about the extent of such variability across many weather years and about the benefits potentially brought about by nearly zero-energy buildings. To fill this gap, this work develops an open-source space-heating demand simulation workflow that is applicable to any country's building stock. The workflow, based on a well-established lumped-parameter thermodynamic model, allows capturing sub-national weather-year variability and the mitigation effects of refurbishment. For Italy, different weather years lead to variations in heat demand up to 2 TWh/day, lasting for several days. Moreover, some weather regimes produce spatial asymmetries that may further complicate heat-electricity integration. The refurbishment of about 55% of buildings constructed before 1975 could substantially mitigate such oscillations, leading to a 31–37% reduction of yearly heat demand, primarily in colder regions. Intra-day heat demand variations, driven by user behaviour, are not substantially impacted by refurbishment, calling for the simultaneous deployment of flexible heat generating technologies.

Effect of a one-dimensional approach in LCA on the environmental life cycle impact of buildings: Multi-family case study in Flanders

The environmental impact of buildings is commonly quantified through life cycle assessment (LCA). Because of practical constraints (e.g. data limitations, timing, complexity), various simplifications are often made. However, the impact of certain simplifications is not clear. Due to the particular geometry of a building, the execution of renovation measures typically entails environmental impacts due to material and energy use at building envelope interfaces which are not considered in simplified LCAs. Hence, this paper examines the effect of a one-dimensional approach as simplification strategy on the environmental impact regarding material use and operational energy use for different renovation scenarios applied to a residential high-rise building. A simplified LCA is therefore compared with a detailed LCA that considers the real building design. First, the effect of linearly scaling the environmental impact based on a one square meter approach is confronted with the real building complexity. Then, the impact of neglecting thermal bridges on the operational energy use is analysed. Finally, both results are combined to evaluate the overall effect on the environmental life cycle impact. The simplifications entail an underestimation of the environmental material use, operational energy use and life cycle impact of 6–49%, 10–36% and 10–30%, respectively. The underestimation of the life cycle impact highly correlates with the operational energy use because of the significant contribution of the operational energy use to the environmental life cycle impact, namely 81–99%. As a result, the effect of simplifications concerning material use on the total life cycle impact is found to be negligible.

What is the optimal robust environmental and cost-effective solution for building renovation? Not the usual one

Buildings are responsible for a large share of CO2 emissions in the world. Building renovation is crucial to decrease the environmental impact and meet the United Nations climate action goals. However, due to buildings’ long service lives, there are many uncertainties that might cause a deviation in the results of a predicted retrofit outcome. In this paper, we determine climate-friendly and cost-effective renovation scenarios for two typical buildings with low and high energy performance in Switzerland using a methodology of robust optmization. First, we create an integrated model for life cycle assessment (LCA) and life cycle cost analysis (LCCA). Second, we define possible renovation measures and possible levels of renovation. Third, we identify and describe the uncertain parameters related to the production, replacement and dismantling of building elements as well as the operational energy use in LCCA and LCA. Afterwards, we carry out a robust multi-objective optimization to identify optimal renovation solutions. The results show that the replacement of the heating system in the building retrofit process is crucial to decrease the environmental impact. They also show that for a building with already good energy performance, the investments are not paid off by the operational savings. The optimal solution for the building with low energy performance includes the building envelope renovation in combination with the heating system replacement. For both buildings, the optimal robust cost-effective and climate-friendly solution is different from the deep renovation practice promoted to decrease the energy consumption of a building.

Development of a tool to predict the socio-economic consequences of better air quality and temperature control in classrooms

A simplified modelling framework for the prediction of the indoor environment, energy use and socio-economic consequences of improving air quality and temperature in school buildings is suggested. The framework combines established models for infiltration and different modes of ventilation to estimate yearly distributions of the classroom temperature and CO2 concentration. These distributions are used as input to a prediction of pupil performance of schoolwork, their attendance at school, and teacher absence before and after refurbishment of their school building. Eventually, the framework estimates the socio-economic consequences of an improved classroom environment for a school and may be used to evaluate the feasibility of a range of different refurbishment scenarios and support decisions on building upgrades.

Optimal design and operation of a building energy hub: A comparison of exergy-based and energy-based optimization in Swiss and Italian case studies

With growing concerns on global warming, exergy-based design methods for energy hubs (EHs) in the urban context have been recently investigated to promote more rational and efficient use of energy sources. This study aims to compare exergy-based multi-objective optimization for energy hubs with two primary energy-based methods. The comparison has been performed for Italy and Switzerland, two countries with diverse markets and national electricity production mixes, to indicate the generalizability of our findings. An apartment building in Vevey, Switzerland, was selected to provide domestic hot water demand and structural data to the space heating demand dynamic simulation, for which different TRY weather data have been used for the two countries. Once a superstructure for the energy supply system had been defined, a MILP framework was developed, minimizing a weighted sum of exergy and cost. Using different weights for the two objectives, a Pareto frontier was obtained for each scenario, defining the best possible trade-off solutions between economic and exergetic objectives. The same optimization methodology was performed using total or non-renewable primary energy as an objective. The use of a boiler and PV panels is preferred when primary energy-based methods are applied, while the use of heat pumps and solar thermal panels is preferred when exergy-based method is applied. As a result, the exergy-based method seems to be the most effective as the carbon intensity of the electricity decreases, providing solutions with lower CO2 emissions and reasonable costs in the future when national electricity production will be gradually decarbonized. In addition, a sensitivity analysis of the exergy method was carried out to analyze the influence of key parameters such as energy prices and energy demand variation on the optimized energy system. In addition, the renovation scenarios of the case study building were presented, revealing different optimal setups of the energy hub as the weight of investment costs increases.

Thermal performance optimization of envelope in the energy-saving renovation of existing residential buildings

Based on the life cycle cost analysis, a thermal performance optimization model of building envelope was developed for the energy-saving renovation of existing residential buildings. The calculation formulas of the economic insulation layer thickness of the roof, external wall and floor over the basement were obtained simultaneously, and the optimal relationship of thermal performance among building envelope units was proposed; that is, the equivalent U-value of each opaque building envelope units should be equal. Moreover, the calculation methods of ultimate and optimum energy saving ratio were put forward. For a case study existing residential building in Beijing, compared to the energy-saving renovation scenarios determined by the limit value method (LVM), the saving ratio of insulation material of those determined by the optimization model ranges from 0.53% to 73.59%; and the saving ratio of insulation investment ranges from 0.21% to 64.93% when the building envelope energy saving ratio been taken the same value and changing from 0.65 to 0.85. The limit U-value of envelopes prescribed by Design Standard for Energy Efficiency of Residential Buildings in Beijing (DB11/891–2012) cannot meet the optimal relationship. Therefore, energy-saving renovation scenario determined by LVM cannot obtain the best economic benefit. The ultimate and optimum energy saving ratio achieved are inversely proportional to the U-value of windows. However, improving the performance of windows cannot necessarily achieve the best economic benefit. It is necessary to consider the technical advancement and economic rationality to choose the windows.

Effect of Retrofit Scenarios on Energy Performance and Indoor Thermal Comfort of a Typical Single-Family House in Different Climates of Morocco

Abstract This paper aims at identifying the impact of three retrofit scenarios of a typical single-family house on its energy performance and its indoor thermal comfort in several climates. Two of these scenarios are based on the Moroccan Thermal Regulation in Constructions (RTCM) while the third is the one proposed in this study. The climates range from group B to group C of the Köppen climate classification. The results show that the proposed renovation scenario allows reducing the heating load by 19–42% and the cooling load by 29–60% depending on the climate. Furthermore, the RTCM retrofit scenario leads to summer overheating in all climates. One of the main reasons for this overheating is the insulation of the slab-on-grade floor as this insulation increases the annual heating/cooling energy needs of the house by 6–10%. Moreover, the cavity wall technique was found to be the best option for external walls, instead of using high thermal insulating material, in hot climates. The analysis of the energy performance, the thermal comfort indices, and the payback periods for each retrofit scenario shows that the proposed scenario presents the best thermal performance, except for the cold climate where the RTCM scenario is the most favorable.

Energetic and economic analysis of a PV-assisted air-to-water heat pump system for renovated residential buildings with high-temperature heat emission system

Air-to-water heat pumps are attractive as a replacement of inefficient fossil fuel-based heating systems in thermally renovated buildings. If existing radiator heating systems are not replaced during renovation, high flow temperatures limit the efficiency of the heat pump. For such systems we analysed the possibility to reduce grid electricity consumption by combining the heat pump with a photovoltaic system, a thermal storage (water tank) and an intelligent rule-based control system that enables targeted heat pump operation with on-site PV electricity. Detailed TRNSYS simulations were carried out for space heating and domestic hot water preparation in a single-family house in Zurich in two renovation scenarios. Different strategies for improved control were analysed and combined to an integrated control approach. To perform an economic analysis, the payback time of the PV system was analysed for different PV and storage sizes. Assuming typical Austrian electricity prizes and feed-in tariffs, the shortest payback time of 17.1 years can be achieved with 5kWp of PV, combined with a storage volume of 1 m3. If operated with this optimum size and the advanced control, the system saves 2400 kWh/a (a reduction of –29%) of grid electricity, and the net cost of electricity is lowered by 585€/a (–35%) as compared to the same system without PV. A sensitivity analysis was performed to consider the situation in different countries, using different electricity prizes and feed-in tariffs. The results show how these influence the payback time and the optimum size of the PV system.

Developing two benchmark models for post-world war II residential buildings

In the context of the European carbon neutrality targets, building benchmarks are a key issue for the renovation of existing buildings. Although there are various benchmark methods for energy efficiency characterization, their application to the residential sector is still limited. This paper developed two building simulation models for post-world war II houses in Belgium based on data from post-occupancy measurements and field survey campaigns. The study reports the energy characteristics and occupancy profiled of detached single-family houses. An analysis of energy consumption (electricity and natural gas) and a walkthrough survey were conducted between 2016 and 2019. The benchmark model’s validity has been further checked against public statistics and verified through model calibration and monthly energy bill comparison. Two reference models representing 633.702 post-WWII single-family houses in Belgium were created and validated. The first archetype has an average energy use intensity of 166 kWh/m2/year and represents detached single-family houses built between 1945 and 1969. The second archetype has an average energy use intensity of 155 kWh/m2/year and represents detached single-family houses built between 1970 and 1990. The paper provides a timely opportunity to evaluate the real performance of post-world war II most common archetypes concerning design assumptions and how building professionals can turn the energy performance gap challenge to their advantage. The findings on energy needs and intensity are useful for creating future renovation scenarios for similar archetypes in Western European countries.

Influence of material choice, renovation rate, and electricity grid to achieve a Paris Agreement-compatible building stock: A Portuguese case study

The thermal retrofit of buildings plays a key role to limit global warming. However, the spatial and temporal dynamics of urban-scale renovation are not well understood. This paper proposes a new methodology that is based on a bottom-up building stock model. It links dynamic Material Flow Analysis with dynamic Life Cycle Assessment to include the temporal dynamics of emissions and renovation activity, and the spatial dynamics of the building stock. Alternative renovation scenarios for a Lisbon neighborhood are analyzed over the next 100 years. Thee scenarios include renovation rates, electricity grid transformation and material choice: Conventional renovation systems are compared to bio-based systems (using cork, wood and straw). A need-based prioritization of poorly insulated buildings is suggested and the effect of different energy grid transitions analyzed. The results show that bio-based systems, especially made with fast-rotation biomass, are beneficial regarding radiative forcing. The straw- and wood-based system ("TES"), combined with an increased renovation rate, result in a cumulative radiative forcing of −45.4 * 10−8 kW/m2 for embodied impacts in 2050, compared to 3.5* 10−8 kW/m2 with a conventional system and a business-as-usual renovation rate. A fast and radical transition of the energy grid is crucial to meet the carbon budget to limit global warming to 2 °C.

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.

RENOVATION OF INFORMAL GREEN SPACES IN OLD URBAN RESIDENTIAL COMMUNITIES IN CHINESE CITIES AND RELATED PUBLIC PERCEPTION INVESTIGATION

Existing large-scale urban green spaces in a low distribution density can hardly meet citizens’ diverse and growing needs for convenient access and sharing modes, especially to those living in old communities. Compared with formal green spaces, informal green space (IGS) is a new urban green infrastructure contributing to the city’s coconstruction, co-governance, and co-sharing. This study was conducted based on a typical old residential community in the historic city center of Beijing, namely Beitaipingzhuang Neighborhood, acquired residents’ opinions, evaluation, and willing to participate in IGS governance, and investigated their preference of IGS renovation, activity, and the positive / negative perception of IGS scenarios through virtual renovation proposals upon the real scenes. According to the survey result, most residents have been aware of the existing IGS in communities as well as the advantages and disadvantages, and shown their support to IGS cogovernance; residents’ preference of IGS renovation scenarios is significantly affected by environmental factors—residents prefer the green spaces with a higher plant richness, a larger crown size, and a more complete leisure facility system. Therefore, residents’ positive perception can be enhanced through enriching plant species, adjusting green space ratio, and introducing proper planting patterns and facility types. Finally, the authors put forward several research interests for following up so as to provide targeted guidelines for the optimization of urban living environment.

Raw materials consumption and demolition waste generation of the urban building sector 2016–2050: A scenario-based material flow analysis of Vienna

Abstract Cities are substantial consumers of raw materials and producers of waste. For this reason, a number of cities including Vienna, defined in their sustainable development strategies a reduction of the raw material consumption as a sustainability target. As the majority of raw materials consumed by cities are used in the building construction sector, buildings play a key role to achieve this target. While many raw material reduction strategies of cities focus on demolition waste recycling, only few studies also consider the reduction of this waste by avoiding the demolition of buildings. Using the case study of Vienna, this article presents the investigation of three urban development scenarios with respect to the demolition of buildings for the time span 2016–2050. The first scenario projects the business-as-usual from the past years (1991–2015), the second foresees a higher demolition of buildings and substitution by new ones, and the third assumes a zero-demolition of selected old buildings, but extensive renovation toward higher thermal insulation standard and attic-extension of these. The results show that in comparison to the year 2015, the business-as-usual reduces the raw material consumption for building construction by −26%, the demolition scenario by −17%, and the renovation scenario by −35%. On a per-capita basis, the reductions are higher. While these results indicate that the renovation scenario is the most suitable to contribute to the sustainable urban development targets in cities like Vienna with respect to the reduction of the consumption of raw material for building construction, further investigations are required to define other measures to finally achieve this target. Furthermore, environmental impacts and land-use of such urban development scenarios should be investigated.