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Rapid environmental and economic changes occurring at the international and regional levels have necessitated the creation of a mechanism to provide an expert evaluation of the building's sustainability. In this regard, building certification methods have been developed to assess their energy efficiency and environmental friendliness. Building certification is one of the tools for sustainable state development. In Azerbaijan, the significant amount of natural resources causes mainly the voluntariness of the building certification process and the passivity of the government and public authorities. The article explores the experience of using the building certification methods BREEAM, LEED, Green Zoom Azeri in Azerbaijan. Technical characteristics of the certified buildings were considered. The benefits and drawbacks of the certification methods used were outlined, along with reasons for boosting the attractiveness of certified buildings. Based on the comparative analysis, the existing issues were classified as people-oriented, technology-oriented, and processoriented. Expert recommendations on overcoming obstacles and eliminating issues are given. The outcomes of this research will contribute to the development of a roadmap for the sustainability of the building industry, and help a wide range of international and local experts to better understand the emerging green building market in Azerbaijan. [ABSTRACT FROM AUTHOR]

The article examines the feasibility of implementing standalone hydrogen-based renewable energy systems in Spanish residential buildings, specifically analyzing the optimization of a solar-battery and solar-hydrogen system for a building with 20 dwellings in Spain. The study initially assesses two standalone setups: solar-battery and solar-hydrogen. Subsequently, it explores scenarios where these systems are connected to the grid to only generate and sell surplus energy. A scenario involving grid connection for self-consumption without storage serves as a benchmark for comparison. All system optimizations are designed to meet energy demands without interruptions while minimizing costs, as determined by a techno-economic analysis. The systems are sized using custom software that incorporates an energy management system and employs the Jaya algorithm for optimization. The findings indicate that selling surplus energy can be economically competitive and enhance the efficiency of grid-connected self-consumption systems, representing the study's main innovation. The conclusion highlights the economic and technical potential of an autonomous hybrid energy system that includes hydrogen, with the significant remaining challenge being the development of a regulatory framework to support its technical feasibility in Spain. • PV-battery and PV-H2 self-sufficient stand-alone systems for a 20-dwelling building. • Residential energy demand based on actual annual four-dwelling metering data. • Sale of surplus generation to the grid from a renewable stand-alone system. • Custom-made software and Jaya algorithm for system simulation and cost optimization. • PV-H2 integration in residential buildings leads to a reduction in grid dependency. [ABSTRACT FROM AUTHOR]

The new wave of performant technology devices generates massive amounts of data. These devices are used in cities, homes, buildings, companies, and more. One of the reasons for digitalizing their tasks is that over the past few years, there has been an interest in reducing carbon emissions and increasing energy efficiency to create a friendly ecosystem and protect nature. One of which granted the explosion of data. After deploying these new devices, a significant increase in the use of the other face of energy to implement the components of the new devices was noticed. Above all, the interconnection of these intelligent devices is the central concept of the Internet of Things (IoT). This domain has widened the possibilities for the interconnection of building management systems (also named Smart Grids) and devices for better energy management. Furthermore, its potential is realized only after organizing and analyzing a large amount of data. Real-time management and maintenance of big data are critical to improving energy management in buildings. The benefits of big data analytics go beyond savings on electricity bills. It can provide comfort for building users and extend the life of building equipment, enhancing the value of commercial buildings. Intelligent interconnection of a building's technical installations (lighting, heating, hot water, photovoltaic installations, etc.) not only allows for connected management of this equipment but also meets high energy efficiency criteria that indicate an increase in comfort and energy savings. With building automation, the technical installations of a building interact optimally. In this article, we will simulate an intelligent building based on the Cisco packet tracer software. To better manage the energy consumption of our project, we will focus on the processing of data in real-time, especially since we will have a massive amount of data generated by the sensors, which makes the use of big data mandatory. [ABSTRACT FROM AUTHOR]

Reducing energy consumption and greenhouse gas emissions to combat global climate change has become an international necessity. A large portion of energy consumption is attributed to old buildings, with increased operational costs and decreased occupational comfort. These buildings require renovation measures that can be viewed as an opportunity to implement sustainable construction and create traditionally designed buildings based on sustainable construction. This study proposes a combination of building information modeling (BIM) with Design Builder and Meteonorm software to evaluate the environmental impact of renovating buildings during the operational phase. The process involved is demonstrated in a case study application to a typical two-story single-family residential building in Tehran city, in which the building's materials, weight, and volume are determined using Revit software, and Design Builder is used to simulate the renovation effect on various building components – showing that, by combining the most effective insulation, glass, heating system, and doors, a 15% reduction in energy consumption and 44% reduction in CO2 emissions could be achieved. The study significantly advances knowledge in sustainable building practices by introducing a practical framework for decision-making in environmentally conscious building renovations, focusing on such crucial elements as insulation materials, window glass types, heating systems, and door materials. It also provides valuable insights into the practical implications of weather data for accurate environmental forecasting, emphasizing the methodology's applicability and adaptability to similar contexts, as evidenced by its successful application in a real-world case in Tehran. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze the economical suitability of numerous on-site renewable electricity generation technologies which were intended to be used in a recently built industrial facility designed and utilized as a warehouse. The facility was located in the vicinity of Riga, Latvia. Data were collected and calculations were performed within the scope of the project "Mitigating Energy Poverty through Innovative Solutions" as part of several planned activities to address the broad spectrum of energy poverty and self-reliance issues in both the residential sector and small-scale industrial facilities. During the project, evaluations of various renewable energy technologies, including PV installations, wind energy installations, battery storage solutions, and hybrid technologies, were carried out. The aim of these evaluations was to develop an electricity production–consumption model for efficient and cost-effective energy use and to reduce greenhouse gas emissions from the test facility. A model was created and subsequent research scenarios were developed based on a payback period instead of the net present value criterion. The project was carried out over several steps to develop a calculation methodology. The open access databases of energy resource providers were used to evaluate statistical data and make forecasts for the analysis of the electricity consumption of companies. MATLAB/Simulink 23/2 was used for the data analysis, and the H-TEC method was employed. This made it possible to modulate the required production capacity as the model allowed for the addition of new modules to modules already installed. The project results proved that despite high initial investment costs, renewable energy sources and efficient storage systems can provide cost-effective solutions and reduce dependence on fossil fuels in the long term. [ABSTRACT FROM AUTHOR]

In 2023, residential and commercial sectors together consumed approximately 27.6% of total United States (U.S.) energy, equivalent to about 20.6 quadrillion Btu. Factoring in the electrical system energy losses, the residential sector represented approximately 19.7% of total U.S. energy consumption during that time. There were approximately 144 million housing units in the United States in 2023, which is increasing yearly. In this study, information on energy usage in the United States residential sector has been analyzed and then represented as energy intensities to establish benchmark data and to compare energy consumption of varying sizes and locations. First, public sources were identified and data from these previously published sources were aggregated to determine the energy use of the residential sector within the US. Next, as part of this study, the energy data for seven houses/apartments from five different United States climate zones were collected firsthand. That data were analyzed, and the energy intensity of each home was calculated and then compared with the energy intensities of the other homes in the same states using Residential Energy Consumption Survey (RECS) data. The energy intensity for each facility was calculated based on the actual energy bills. Finally, the study evaluated the carbon footprint associated with residential energy consumption in all 50 states to reinforce the importance of sustainable development initiatives. [ABSTRACT FROM AUTHOR]

The main objective of the research presented in the following work was the adaptation of reflection-radar technology in a detection and navigation system using radio-tomographic imaging techniques. As key aspects of this work, the energy optimization of high-frequency transmitters can be considered for use inside buildings while maintaining user safety. The resulting building monitoring and control system using a network of intelligent sensors supported by artificial intelligence algorithms, such as logistic regression or neural networks, should be considered an outcome. This paper discusses the methodology for extracting information from signal echoes and how they were transported and aggregated. The data extracted in this way were used to support user navigation through a building, optimize energy based on presence information, and increase the facility's overall security level. A band from 5 GHz to 6 GHz was chosen as the carrier frequency of the signals, representing a compromise between energy expenditure, range, and the properties of wave behavior in contact with different types of matter. The system includes proprietary hardware solutions that allow parameters to be adjusted over the entire range and guarantee adaptation for RTI (radio tomography imaging) technology. [ABSTRACT FROM AUTHOR]

Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees' decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

With the increasing need for energy, issues related to using energy efficiently in buildings and employing renewable energy technologies are gaining significance. The building production process is intricate, involving numerous stakeholders, multiple decisions, and a combination of qualitative and quantitative data. This process necessitates decision making based on specific requirements. The objective of this study is to identify effective criteria in decision making concerning the use of renewable energy technologies (RETs) in buildings in Turkey. It aims to highlight the importance of these criteria and compare them, and it also aims to define a recommendation decision-making model for the widespread adoption of renewable energy technologies. This study employed qualitative and quantitative research methods. Based on information gathered from the literature, the main criteria and sub-criteria for RET utilization were determined through in-depth interviews with an expert group, including individuals influencing the building design process (architects, engineers, consultants, employers, and users). A recommendation model was developed using the analytic hierarchy process method to highlight the significance of the identified criteria, compare the criteria and technologies, and facilitate the selection of the most-appropriate technology. This study demonstrates that the decision-making model can be utilized in determining RET-related criteria in the building production process, establishing their weights, and make informed decisions regarding the appropriate technology. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) hold great promise for diverse applications when combined with polymers. However, a persistent challenge lies in the susceptibility of exposed MOF pores to molecule and polymer penetration, compromising the porosity and overall performance. Here, we design a molecular-caged MOF (MC-MOF) to achieve contracted window without sacrificing the MOF porosity by torsional conjugated ligands. These molecular cages effectively shield against the undesired molecule penetration during polymerization, thereby preserving the pristine porosity of MC-MOF and providing outstanding light and thermal management to the composites. The polymer containing 0.5 wt % MC-MOF achieves an 83% transmittance and an exceptional haze of 93% at 550 nanometers, coupled with remarkable thermal insulation. These MC-MOF/polymer composites offer the potential for more uniform daylighting and reduced energy consumption in sustainable buildings when compared to traditional glass materials. This work delivers a general method to uphold MOF porosity in polymers through molecular cage design, advancing MOF-polymer applications in energy and sustainability. [ABSTRACT FROM AUTHOR]

In this study, the thermal comfort and energy consumption of two different terminal units when combined with three different water supply temperatures were studied. Results showed that convection–radiation combined cooling terminals can increase the water supply temperature under the same thermal comfort level compared with convection cooling terminals. When the water supply temperature was fixed at 11 °C, the energy consumption of convection–radiation combined cooling terminals was 9.1 % lower than that of convection cooling terminals. The higher the water supply temperature, the lower the energy consumption under convection–radiation combined cooling terminals. Furthermore, the energy consumption rates at a water supply temperature of 15 °C were 18.2 % and 8.8 % lower than those at 7 °C and 11 °C, respectively. Meanwhile, the comfort and system operation efficiency were optimal when the water supply temperature was 15 °C, and the energy consumption was lower when combined with the cooling of the dual terminals. [ABSTRACT FROM AUTHOR]

In smart buildings, dynamically controlled lighting and shading devices have a direct impact on occupants' thermal and visual comfort, building energy use, and peak demand. This study aims to assess the impacts of adjusting vertical and work plane illuminance sensor location and rotation for different zone orientations across different climate zones, on illuminance levels, glare, energy consumption, and demand. Using the sensor locations of 1.6 m and facing the window (typical assumption), when implementing the integrated shading and lighting controls, the lighting and cooling energy savings range from 41% to 71% across both heating and cooling-dominated climate zones. The greatest cooling peak use demand reduction of 6.5 kW (23%) is achieved in Climate Zone 2 A, where the total cooling and lighting demand savings are 28-33% (9-10 kW). The percent change in lighting energy use is greater as compared to the percent change in cooling energy use when sensor location is varied, however, the total kWh and kW change is greater for cooling in most climate zones. Results suggest that adjusting the sensor distance from the window has a greater impact on illuminance levels, glare, and lighting and cooling energy use in all climate zones as compared to sensor rotation. [ABSTRACT FROM AUTHOR]

In Poland and other countries in Central Europe, residential buildings from the second half of the 20th century dominate, which have recently undergone deep thermomodernisation. Research on the retrofitting of residential buildings has focused mainly on energy efficiency, with only a few studies on indoor air quality. The aim of this study was to present a comparative analysis of the impact of five ventilation scenarios (three natural and two mechanical) on CO2 concentration and energy demand for heating and ventilation in residential spaces of a multi-family building located in Poland. The analyses were based on the results of building performance co-simulation using the EnergyPlus and CONTAM programs carried out under dynamic conditions with a 5 min time step for the entire heating season. The calculations took into account the instantaneous occupancy variability of twenty apartments. In the buildings equipped with new tight windows, the natural ventilation system provided extremely low air exchange (on average 0.1 h−1) and poor indoor air quality (average CO2 concentration at the level of 2500 ppm). Opening windows to ventilate the rooms generated a multiple increase (up to 8 times) in heating demand during these periods, but average CO2 concentration was on the level of 930 ppm. The use of mechanical ventilation was profitable both in terms of energy savings (at the level of 50%) and improvement in the indoor air. [ABSTRACT FROM AUTHOR]

Despite a gradual decline in rural population due to urbanization, as of 2022, approximately 35% of China's total population still resides in villages. Over a span of 40 years, carbon emissions from villages have significantly surged, with a sevenfold increase from energy consumption and a 46% rise from agriculture. Consequentially, the development of low-carbon villages is imperative. A comprehensive understanding of the primary sources of carbon emissions in villages is crucial for implementing practical and effective strategies towards low-carbon development. However, limited research has been conducted on quantifying carbon emissions and sinks for Chinese villages. This study aims to address this gap by proposing a methodology for assessing carbon emissions in villages, including the emissions of CO2, CH4 and N2O. Inspired by the IPCC standard methodology for greenhouse gas emissions at national levels and provincial greenhouse gas inventory guidelines customized for China's context incorporating localized characteristics, this approach has been applied to seven villages in Northern China based on field investigations. Employing a range of methods including field surveys, questionnaires, statistical records and big-data platforms, we collected the carbon emission activity levels of the seven villages using the most up-to-date carbon emission factors. Subsequently, the collected data and facts are quantitatively processed to generate results that are compared among the seven villages. These findings are also compared with those from other studies. The analysis indicates that the primary industries in these villages significantly influence the total carbon emissions. Moreover, the study reveals that energy consumption in buildings, agriculture, transportation and waste disposal are the most influential emission sources. These findings provide valuable insights into the carbon emission landscape of villages and can serve as a guide for implementing strategies and policies aimed at promoting low-carbon development in the rural areas of Northern China. [ABSTRACT FROM AUTHOR]

Ensuring optimal building performance is vital for enhancing student activity comfort and fostering energy-saving initiatives toward low-carbon objectives. This paper focuses on university student centers in China, aiming to diminish building energy consumption while enhancing indoor thermal comfort. Parametric modeling of typical cases is executed using the Grasshopper 1.0.0007 software package, and the simulation of building energy consumption and indoor thermal comfort relies on the Ladybug and Honeybee plug-in. Employing a multi-objective optimization design method and the Octopus multi-objective optimization algorithm, this study integrates numerical simulations and on-site surveys to analyze how factors like building form, orientation, envelope structure, and others impact the indoor and outdoor environment. A comprehensive optimization design approach is implemented for the building's exterior components, including the walls, windows, roof, and shading system. After conducting a comparative analysis of the annual comprehensive energy consumption and indoor thermal comfort before and after the optimization plan, it is determined that implementing these measures reduces the annual comprehensive energy consumption of the building under study by 58.8% and extends the duration of indoor thermal comfort by 53.0%. This study presents a practical optimization design methodology for university student center architecture in China, aiding architects in decision making and advocating for energy-efficient building designs. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of buildings is one of the main challenges facing Europe in the context of climate neutrality policy. In this article, the authors decided to investigate the impact of thermal insulation of building envelopes and the type of ventilation on the energy efficiency of multi-family buildings located in different Polish cities. In accordance with EU directives, economic analyses of the costs incurred during the lifecycle and calculations of C O 2   emissions were carried out for the analyzed facilities. It was determined what measures need to be taken under Polish climatic conditions in order to meet the requirements for improving the energy efficiency of multi-family buildings. The multi-criteria analyses presented in this article provide a voice in the discussion of the issues related to the achievement of the energy performance of buildings with almost zero primary energy demand (nZEB). Based on the conclusions of the research conducted, it was possible to develop general recommendations for investors and designers of multi-family residential buildings characterized by energy efficiency and reduced environmental impact. [ABSTRACT FROM AUTHOR]

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems. [ABSTRACT FROM AUTHOR]

Earth's temperature has risen by ≈0.18 °C per decade since 1981 and year 2022 is the sixth‐warmest year on record. The exploration of cooling strategies that operate with less energy consumption is thus highly desirable. Through this project, a camel‐fur‐inspired passive membrane encapsulated sorbent cooler that can periodically absorb moisture from the atmosphere and release it for daytime evaporative cooling, leading to effective regulation of building temperature without additional energy input is demonstrated. This sorbent cooler with high sorption capacity is made of anhydrous salt and modified expanded graphite and further encapsulated by a breathable porous membrane. This novel material is shape‐stabilized, and it has excellent cycling performance, and remarkable water adhesion ability. In this evaporative‐cooling demonstration, the average cooling power of the camel‐fur‐inspired sorbent can reach up to 630 W m−2, which is much higher than the radiative cooling approach. This theoretical model implies that the proposed sorbent cooler has the potential to cut down cooling energy carbon emission by 61% to 87.83% when compared to emissions released from cooling energy usage in reference buildings. This nature‐inspired approach paves a new way for efficient space cooling without extra water supply and with minimized energy consumption. [ABSTRACT FROM AUTHOR]

As the proportion of buildings energy consumption increases, the issue of low energy utilization efficiency becomes increasingly prominent. Approximately 45% of the energy is lost through doors and windows, a significant concern that has drawn society's attention to the potential of electrochromic technology, which can regulate light transmittance and absorption effectively. Specifically, dual‐band electrochromic materials offer a promising solution to this issue, as they can manipulate the transmission ratios of visible (VIS) and near‐infrared (NIR) light to obtain "bright," "cold," "warm," and "dark" independent modes. However, research on dual‐band electrochromic materials and smart windows is still infant, and many challenges including performance and assembly issues, are significant obstacles to the advancement and application of this technology. This review clarifies the basic characteristics of dual‐band electrochromic materials and smart windows, including configurations, operational mechanisms, and performance assessment parameters. In‐depth examination of potential dual‐band electrochromic materials and methods for achieving independent modulation is provided, along with an analysis of the challenges and potential solutions for performance and assembly issues. Finally, the future prospects of dual‐band electrochromic smart windows (ESWs) are underscored. The hope is to eventually facilitate the commercial application of these windows, which can significantly contribute to the creation of modern, energy‐efficient buildings. [ABSTRACT FROM AUTHOR]

The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES's parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%. [ABSTRACT FROM AUTHOR]

In this study, the electrical energy consumption of an apartment building was calculated depending on different design parameters. The electrical energy consumption was found according to the population information of each of four cities selected to represent different climatic regions. The calculations took into account, firstly, the application of different insulation materials; secondly, the state of the building: discrete, surrounded by trees or adjacent to another building; and, thirdly, different glazing types. Using the Taguchi method, experimental designs were conducted for each representative city. Then, the most suitable building design parameters were determined for each city according to signal-to-noise ratios. In the study, pollutant emissions, such as carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxides (NOx) and particulate matter, released to the atmosphere were investigated. It was observed that there was a 50% reduction in energy consumption and emissions based on the most suitable building design parameter determined by the Taguchi method. [ABSTRACT FROM AUTHOR]

In this study, three identically designed model buildings and their heating energy consumption was compared. Those pilot scale buildings are equal by size and were located on the same location. During the measuring campaign both external and internal temperatures were recorded, beside the energy consumption. The model buildings were labeled as A (constant baseline), B (baseline varying according to a time program), and C (unheated, blank). During the measurements, the thermal mass of the buildings was altered. The same amount of thermal mass was installed in all three model buildings during all measurements. According to our results under real weather conditions, the intermittent heating requires less energy than maintaining a constant temperature, and the energy saving is inversely proportional to the time constant at intermittent heating. Instead of specific heat mass, a thermal time constant was used to compare intermittent and constant heating. It was established that as the thermal constant of the model building increases, the energy savings between maintaining a variable base temperature and maintaining a constant base temperature decrease. The expected savings are between 4% and 7%. [ABSTRACT FROM AUTHOR]

The aim of the study is to analyse energy consumption for heating in set of the school buildings. Data analysis includes three data sets – predictions from energy performance certificates (EPC), measured data from heat meters and measured climate corrected data. Data sets were statistically checked and processed and represented graphically. The actual energy consumption differs from the one stated in the EPCs in all cases, which means that schools consume on average 15 % more energy than it is stated in the EPC. Overall, the data reveal that the actual energy consumption differs from the theoretical consumption, with all schools having higher actual consumption than the theoretical values. The differences in the data sets are discussed, and possible improvements are suggested taking into account strong relation between energy consumption and indoor air quality. [ABSTRACT FROM AUTHOR]

After the settlement textures were designed and constructed, they continue to exist without any change for years, and they have considerable effects on building energy consumption and the natural environment. With the changes in our lives following the COVID-19 global pandemic, employers have adopted working from home in many sectors, and energy expenditures in residences are increasing even more. The decrease of energy usage is a top objective in the design of the settlements from an economic and environmental standpoint. Also, while known energy modeling methods require extra expertise, labor and time in the integration of energy efficient design strategies into design processes, BIM systems have a significant potential in the evaluation of building energy efficiency. In this study, for the comprehensive evaluation of design parameters, different settlement texture alternatives, have been developed and building design parameters determined. These settlement textures were interpreted based on their annual energy consumption. By comparing the results, optimum values were sought for the settlement texture design parameters which are H/W, orientation, building height, and form factor. In addition, energy modelling and simulations were carried out with BIM software, and the use, advantages and capabilities of BIM systems were tested in the energy efficient design for reducing energy consumption. As a result, energy consumption in buildings get changes greatly in regard to combination of settlement scale design parameters. Alternative with form factor 3.00,10 storey, directed to east-west, and H/W 0.5 showed best performance and provided 7% to %17 energy reduction according to different parameter combinations. [ABSTRACT FROM AUTHOR]

College buildings have unique characteristics compared with school buildings. Therefore, defining the realistic occupancy schedule in a prototype college building has significant research opportunities. In this study, the actual operating schedules of each space type were collected and generated based on the class reservation schedule and compared with the previous reference schedule (primary/secondary school). The schedules were analyzed for their effect on the OpenStudio prototype college building model. The findings highlight that the use of a typical school building schedule in a college building impairs the granularity of information. The analysis shows significant differences between the previous occupancy schedule and the updated occupancy schedule of the college building, leading to a considerable decrease in occupancy density. Furthermore, the effect of these occupancy pattern changes on the prototype building model is examined. The variations were observed in minimum ventilation requirements, the average mechanical ventilation rate, and energy consumption attributed to changes in occupancy density. [ABSTRACT FROM AUTHOR]

Energy efficiency in buildings has a central role to play in reaching the climate neutrality goal set by the European Green Deal. With detailed knowledge of their building stock and the profile of their occupants, regional and local authorities can forge an important link between financiers, industry professionals and homeowners to ensure the successful deployment of financial schemes that support the uptake of energy efficiency upgrades. This paper provides a first assessment of the role of regional and local authorities by reviewing relevant initiatives and programmes on energy efficiency. Based on a questionnaire completed for more than 150 schemes, it explores how European municipalities and regions stimulate energy upgrades in residential, commercial and public buildings through public financial support. It is found that 60% of the examined schemes are offered in the form of grants and subsidies, and 45% of them targeted residential upgrades. The use of EU cohesion policy funds in supporting regional schemes, and promotion of energy efficiency investments at local level through the European Covenant of Mayors initiative are also explored. In spite of possible resource limitations at this level of governance, regional and local authorities are in a good position to utilise European structural or research funds to develop financial schemes, as standalone programmes or blended with national ones, thus providing additional support and funds for deep renovations. The results suggest that energy efficiency in buildings has become an important part of local and regional strategies in several European countries, but could be further enhanced. [ABSTRACT FROM AUTHOR]

The present study aims to analyze the correlation between ground motion parameters and energy demands of low-rise RC buildings without shear walls. Two regular 4- and 7-story residential buildings were seismically designed to represent low-rise RC buildings. In order to establish the requirements of single degree of freedom (SDOF) systems as well as multi degree of freedom (MDOF) systems, the dynamic features of "equivalent" SDOF systems were defined by using MDOF systems. The correlation of 20 ground motion parameters (GMPs) of 44 records with the energy demands obtained from a total of 176 nonlinear time history analyses was examined for the SDOF and MDOF systems within the scope of this study. The ground motion parameters (GMPs) were taken as intensity measures (IMs). In contrast, maximum input energy was used as a demand measure (DM) and these energy values were normalized with the masses of the buildings for cases, where the buildings were evaluated together. Parameters related to acceleration and velocity are generally found to yield better results in comparison to the ones related to frequency and displacement. Velocity Spectrum Intensity (VSI) and Arias Intensity (Ia) were obtained to have the highest correlation values as a single parameter. This study suggested new equations by combining multiple ground motion parameters for SDOF and MDOF systems to better reflect damage potential than a single parameter. The use of multiple parameters in combination results in better correlation coefficients. [ABSTRACT FROM AUTHOR]

To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building's four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions. [ABSTRACT FROM AUTHOR]

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption. [ABSTRACT FROM AUTHOR]

The energy efficiency of existing buildings is a crucial factor in addressing energy consumption challenges in European countries, accounting for nearly 40% of the total energy usage. One such country is Cyprus, which faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. To face this situation, extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings erected before 2010, which lack any energy efficiency measures. This study examines the case of the Pano Polemidia Cultural Hall (PPCH), which represents the building stock of that period. Through the simulation of two scenarios, before and after the adoption of retrofit measures, the existing energy performance is initially evaluated and then the adoption of sustainable solutions, which improve substantially the energy efficiency and can be easily adopted from the relevant authorities, is explored. These retrofit measures include installation of HVAC system, covering of the shell of the building with external thermal insulation, lighting replacement with LED devices, installation of PV system and solar panels, and replacement of the external openings with aluminum windows. The results derived show that the energy consumption of the building was reduced from 468 to 218 kWh/m2·yr, with renewable energy sources (RESs) contributing 177 kWh/m2·yr, the CO2 emissions were reduced from 136.73 to 11.5 kg/m2·yr, while the reduction in energy consumption per sector ranged from 25% in lighting to 83% in hot water. Therefore, it is evident that a comprehensive retrofitting plan can transform the PPCH into a near-zero energy consumption building that also provides value to the local community and can act as a successful example for any other non-residential buildings with similar characteristics. [ABSTRACT FROM AUTHOR]

As the impact of climate change intensifies, meeting the energy demand of buildings in China's cold regions is becoming increasingly challenging, particularly in terms of cooling energy consumption. The effectiveness of integrating phase change material (PCM) into building envelopes for energy saving in China's cold regions is unclear. The aim of this study is to assess the effectiveness of PCM integration in building enclosures for energy efficiency in these regions. The research monitored and recorded indoor temperature data from typical residential cases from May to September. This measured data was then used to validate the accuracy of EnergyPlus22-1 software simulation models. Subsequently, the calibrated model was utilized to conduct a comparative analysis on the effects of PCM on indoor temperatures and cooling energy consumption across these regions. The results of these comparative analyses indicated that PCM can alleviate indoor overheating to varying degrees in severe cold regions of China. Focusing on north-facing bedrooms, applying PCMs reduced the duration of overheating in non-air-conditioned buildings in severe cold regions of China by 136 h (Yichun), 340 h (Harbin), 356 h (Shenyang), and 153 h (Dalian). In terms of cooling energy consumption, the energy saved by applying PCMs ranged from 1.48 to 13.83 kWh/m2. These results emphasize that the performance of PCM varies with climate change, with the most significant energy-saving effects observed in severe cold regions. In north-facing bedrooms in Harbin, the energy-saving rate was as high as 60.30%. Based on these results, the study offers guidance and recommendations for feasible passive energy-saving strategies for buildings in severe cold and cold regions of China in the face of climate change. Additionally, it provides practical guidance for applying PCMs in different climatic zones in China. [ABSTRACT FROM AUTHOR]

In order to understand the energy-saving system of building cooling and heating integration, the author proposes a research on an energy-saving system of building cooling and heating integration based on building information modelling (BIM) technology. The author first analyzed the energy-saving performance of the system by establishing models for each module based on meteorological data simulated on the DeST software platform. Secondly, in order to analyze the energy consumption of the integrated system, it is necessary to establish a residential building model, and select the building model in the cold winter and hot summer areas of a city for analysis. The results show that due to the system's heat storage function, there is no need to consume electricity in spring and autumn. Compared to the electric heating hot water mode, the integrated system can save 1198.85 kWh of electricity in non-cooling and heating seasons, with a 100% energy-saving rate. Compared to not using an integrated system, it can achieve an energy-saving rate of 81.9% throughout the entire refrigeration and heating season, with significant energy-saving effects. The integrated heating, cooling and hot water system of BIM technology adopts the technology of cool storage and heat storage coupled with solar energy and heat pump technology. The system has simple structure, obvious energy-saving effect, good environmental protection, and can adapt to various climates throughout the year. The introduction of underfloor heating also increases the comfort of buildings and residences. [ABSTRACT FROM AUTHOR]

According to the European Energy Efficiency Directive for Buildings, member states are required to develop long-term strategies to adopt more sustainable, secure, and decarbonized energy systems in buildings by 2050. In this line of approach, an optional common regime has been established to define and calculate the smart readiness of buildings and assess their ability to adapt their operation to the needs of the occupants and the network. Thus, the smart readiness indicator (SRI) emerged, which assesses technological readiness by examining the presence and evaluation of the functionality level of various smart services, aiming at energy savings, the ability of the building to respond to users' needs, and energy flexibility. This paper focuses on examining the SRI calculation methodology's application to an office building, which is currently being deeply renovated. Initially, there is an analysis of the SRI, its calculation methodology, and its goals. This is followed by the practical calculation part of the SRI for a typical office building located in Greece and belonging to the climate zone of southern Europe. The results indicate that the SRIs application is not a straightforward issue since parameters that need to be considered are not regulated to the same degree. On the other hand, SRI can provide a stimulus for exploiting the renovation potential of buildings, precisely by integrating the various aspects and linking those to the use of innovative technologies. [ABSTRACT FROM AUTHOR]

This study aims to theoretically explore the technological systems of tropical island zero-carbon building (TIZCB) to scientifically understand the characteristics of these buildings in terms of energy utilization, energy conservation, energy production, and intelligent system mechanisms. The purpose is to address the inefficiencies and resource wastage caused by the traditional segmented approach to building energy consumption management. Thus, it seeks to achieve a comprehensive understanding and application of the zero-carbon building (ZCB) technology system. This article focuses on the demands for energy-efficient comfort and innovative industrialization in construction. Through an analysis of the characteristics of TIZCB and an explanation of their concepts, it establishes a theoretical framework for examining the system mechanisms of these buildings. Additionally, it delves into the energy utilization, energy conservation, energy production, and intelligent system from macro, meso, and micro perspectives. This approach results in the development of an implementation strategy for studying the mechanisms of energy usage, conservation, and intelligent production systems in TIZCB. The results show that: (1) this study delves into the theoretical underpinnings of TIZCB, emphasizing their evolution from a foundation of low-carbon and near-zero energy consumption. The primary goal is to achieve zero carbon emissions during building operation, with reliance on renewable energy sources. Design considerations prioritize adaptation to high-temperature and high-humidity conditions, integrating regional culture along with the utilization of new materials and technologies. (2) A comprehensive technical framework for TIZCB is proposed, encompassing energy utilization, conservation, production capacity, and intelligent systems. Drawing from systems theory, control theory, and synergy theory, the research employs a macro–meso–micro analytical framework, offering extensive theoretical support for the practical aspects of design and optimization. (3) The research implementation plan establishes parameterized models, unveiling the intricate relationships with building performance. It provides optimized intelligent system design parameters for economically viable zero-carbon operations. This study contributes theoretical and practical support for the sustainable development of TIZCB and aligns with the dual carbon strategy in China and the clean energy free trade zone construction in Hainan. [ABSTRACT FROM AUTHOR]

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems. [ABSTRACT FROM AUTHOR]

The energy consumption from the building sector accounts for more than 21% of the total in China, where the energy-use intensity of public buildings could reach 3 times that of residential buildings. Energy efficiency improvement (EEI) is considered to be one of the most rapid and cost-effective means for the building sector to achieve carbon neutrality. The Chinese government has carried out a series of policy tool exploration to promote the energy efficiency improvement efforts by public building owners since over a decade ago, but encountered various problems and obstacles. This paper investigated and compared the current status challenges, and problems in the implementation of EEI policy tools in China and developed countries through literature review, field investigations and interviews with experts and stakeholders, and finally also put forward some possible coping strategies for Chinese government. Results indicated that energy grading and labeling, energy benchmarking, carbon-emission trading and energy-consumption quota management were the main 4 EEI policy tools designed based on measured energy consumption data, where the first 3 ones had been widely used over the developed countries but the last one was unique to China. Difficulty in large-scale collection of building energy-consumption data, lack of scientific methods on building energy-use level evaluation, limited data application and feedback, undesirable association between stakeholders, etc., were the main obstacles for achieving the long-term effectiveness of the EEI policy tools. Cooperating with energy supply enterprises to collect data, establishing an effective energy grading system and a closed loop of policy implementation, exploring a new model of building carbon-emission trading and a differential pricing mechanism, combined with innovative financial supports, might be the solutions. The main contribution of this study is that a comprehensive investigation and analysis on the current development status, obstacles, and possible solutions of the public building EEI policy tools in China was carried out, which is expected to provide a reference for the government of China and even the world. [ABSTRACT FROM AUTHOR]

In buildings, air conditioning and mechanical ventilation (ACMV) systems are the major shareholders of overall energy consumption. Energy-efficient designs for ACMV systems in building applications are therefore needed. While designing an efficient ACMV system, consideration must be given to the growing concerns of enhanced thermal comfort and improved indoor air quality. The variable refrigerant flow (VRF) air-conditioning system is a widely adopted alternative to the existing building cooling systems due to the higher energy efficiency and individualized temperature control feature. However, it still suffers from shortcomings such as no outdoor air induction for ventilation and higher initial cost. Therefore, this paper reviewed the variable refrigerant flow and mechanical ventilation/air distribution systems, their integrated designs for non-residential buildings, performance evaluation and control optimization of the integrated systems, VRF systems' faults detection and diagnosis, current application of the VRF systems, and associated challenges. Together with these all, some advanced buildings' cooling techniques and improvements toward nearly/net-zero energy buildings are briefly discussed. Indoor thermal comfort models and criteria for different climates are also presented for an in-depth understanding of the VRF integrated mechanical ventilation designs. The literature survey shows that the supply air temperature and airflow rate are foremost in parameters that can be optimized in VRF integrated ventilation design as they greatly reduce the energy consumption. Further, policies on elevated indoor temperatures in air-conditioned buildings to mitigate their carbon footprint are strictly being implemented. Therefore, this review provides an insight to the researchers for further improvement in the integrated design and control optimization of the parameters involved. A paradigm shifts from the conventional compression-based electric-powered air conditioning systems to the renewable energy driven advanced air conditioning technologies which is also an emerging research area to be focused on achieving the target of nearly/net-zero energy buildings. [ABSTRACT FROM AUTHOR]

The use of air conditioning systems is a major contributing factor to Egypt's growing electricity consumption, which is a serious problem, particularly in arid and hot regions like Aswan City. This study compares the energy-saving capabilities of two air conditioning systems for office buildings in Aswan City: a variable refrigerant flow system (VRF) and a minisplit air conditioning (MSAC) unit. TRNSYS software is used in the analysis. Examining their energy efficiency and applicability to power usage concerns is the primary objective. The principal findings are unambiguous. Particularly in the summer when it's hot outdoors, the VRF system consistently demonstrates a discernible benefit in terms of energy efficiency. By converting to the VRF system, a substantial yearly energy savings of roughly 19,187.43 kWh can be realized. The projected energy efficiency ratio between the MSAC system and the VRF system is 0.746. The VRF system conserves more energy than the MSAC system by 25.4%, which demonstrates how energy-efficient it is. With 8.29-year payback times, the VRF system performed better than the MSAC system. Furthermore, during the period of the cooling system’s life, the VRF system greatly reduced CO2 emissions; when fuelled by coal, oil, or natural gas, respectively, this reduction came to 26.03% (14.58, 11.59, and 8.23 metric tonnes) less than the MSAC system These outcomes highlight the environmental advantages of the VRF system and its potential as an energy-efficient and ecologically friendly cooling solution. [ABSTRACT FROM AUTHOR]

The use of natural-based exterior thermal insulation systems constitutes a significant challenge for achieving energy efficiency of construction. The purpose of this article is to propose new exterior thermal insulation solutions based on natural materials such as wood wool, cellulose wadding, expanded cork, hemp fiber, and sheep's wool, in order to minimize energy consumption, address durability concerns, maintain thermal comfort, and promote the use of natural materials in thermal insulation system designs. The methodology followed consists of presenting a comparative numerical study of different exterior insulation techniques (ETI) using a dynamic thermal simulator for desert regions. The study evaluated ETI systems for vertical walls attached to a concrete block wall, including one EPS system with coating (wet process) and six systems with cladding (dry process). The results show that applying exterior insulation to walls using the two cladding systems based on hemp fiber and sheep wool resulted in a total reduction in building energy consumption of 42.21% and 42.81%, respectively. These results confirm the effectiveness of natural materials in improving the energy performance of buildings, particularly the system based on sheep wool. [ABSTRACT FROM AUTHOR]

Climate change is expected to significantly affect countries in Southern Europe and the Mediterranean Basin, causing higher-than-average temperature increases, considerable reductions in rainfall and water runoff, and extreme events such as heatwaves. These pose severe threats to local energy systems, requiring a reliable and quantitative risk analysis. A methodological approach is thus proposed which covers both energy supply and demand, utilizing the latest climate projections under different greenhouse gas emissions scenarios and an appropriate scale for each energy form. For energy supply technologies, risks are assessed through statistical regression models and/or mathematical equations correlating climatic parameters with energy productivity. To analyze climate risks for energy demand, bottom–up models were developed, integrating both behavioral and policy aspects which are often considered in a very limited way. The results show that climate change will mainly affect electricity generation from hydroelectric and thermal power plants, with variations depending on the plants' locations and uncertainties associated with precipitation and runoff changes. The climate risks for solar and wind energy were found to be low. Energy consumption will also be affected, but the range of risks depends on the ambition and the effectiveness of measures for upgrading the thermal performance of buildings and the intensity of climate change. [ABSTRACT FROM AUTHOR]

The Energy Performance of Buildings Directive (EPBD) is the European Union's main policy instrument for reducing the built environment's contribution to climate change. Energy Performance Certificates (EPC) are a tool of this policy, providing buildings with a performance rating. In the UK these have become the benchmark by which the Minimum Energy Efficiency Standards (MEES) are judged, rendering residences lower than an E illegal to rent out. This has pushed property owners to undertake energy retrofitting interventions to ensure compliance. In traditional buildings this is potentially problematic. Buildings of this type rely on permeability to control moisture dynamics; the insertion of less permeable barriers can interrupt their internal hygrometry leading to condensation, damp and decay. As a result, subsequent damage to the building fabric can bring energy policy into conflict with heritage conservation. Existing frameworks that attempt to mitigate the tension within these agendas often fall short in explicitly considering the the problem holistically. This research first asks if pressure from policy is leading to incompatible retrofits. Secondly, it aims to develop a holistic framework that unites heritage and energy agendas with current policy. An inductive, pragmatic approach was undertaken. Using a historic estate's residential portfolio as a case study, condition surveys were carried out to assess whether properties retrofitted with secondary glazing were experiencing problems with condensation. The results of this were significant, supporting the argument that energy policy is driving incompatible retrofitting interventions. Exploratory data analysis was carried out on an existing EPC dataset to highlight inconsistencies with the EPC methodology. Finally, these findings were combined with questionnaire survey results, targeted at both professionals and occupants, to inform a decision-making framework that incorporated energy efficiency, heritage values, life cycle cost analysis (LCC), building assessment, occupant behaviour, with a feedback loop promoting a bottom-up approach to policy. This issue contains Part 2 which will deal with the methodology and results. Part 1 in the previous issue set the scene. [ABSTRACT FROM AUTHOR]

Traditional buildings have a central role to play in Europe's plans to be the first climate-neutral continent by 2050, with the building sector being the single largest energy consumer in the EU with 40 per cent of total energy use. Seventy-five per cent of the existing housing stock is energy inefficient with minute numbers renovated each year. Existing buildings offer huge potential for energy use reduction. Research has shown that a historic retrofit can emit less carbon compared to demolishment and rebuilding. Reductions in carbon emissions of up to 62 per cent were observed across a body of case studies by Historic England, including a 95 per cent reduction in the mews case study described in this paper. Moreover, carefully managed thermal retrofits, taking a 'whole house approach', have many benefits, including improved indoor comfort and air quality as well as reduced operational costs. These are factors which are fundamental in ensuring the continued use of historic constructions while improving their standards of preservation and durability. Thermally upgrading traditional building is still a much-debated topic. Inappropriate thermal interventions with unsuitable materials and finishes can have unforeseen consequences, resulting in building damage and a reduction in internal comfort, health and well-being. This paper aims to contribute to this discussion by providing an overview of key considerations when thermally upgrading the walls of traditional buildings while preserving unique characteristics and functionality. Advantages of adopting thermal mortars such as cork lime are put forward, and their potential efficacy is discussed, concluding with a case study focusing on the deep retrofit of a London mews within a conservation area. [ABSTRACT FROM AUTHOR]