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The growing demand for sustainable and energy-efficient buildings has highlighted the need for reliable and accurate methods to detect fenestration deterioration and assess UV radiation transmission. Traditional detection techniques, such as spectrophotometers and radiometers, discussed in Part I, are often expensive and invasive, necessitating more accessible and cost-effective solutions. This study, which is Part II, provides an in-depth exploration of the concepts and methodologies underlying UV bandpass-filtered imaging, advanced image processing techniques, and the mechanisms of pixel transformation equations. The aim is to lay the groundwork for a unified approach to detecting ultraviolet (UV) radiation transmission in fenestration glazing. By exploiting the capabilities of digital imaging devices, including widely accessible smartphones, and integrating them with robust segmentation techniques and mathematical transformations, this research paves the way for an innovative and potentially democratized approach to UV detection in fenestration glazing. However, further research is required to optimize and tailor the detection methods and approaches using digital imaging, UV photography, image processing, and computer vision for specific applications in the fenestration industry and detecting UV transmission. The complex interplay of various physical phenomena related to UV radiation, digital imaging, and the unique characteristics of fenestration glazing necessitates the development of a cohesive framework that synergizes these techniques while addressing these intricacies. While extensively reviewing existing techniques, this paper highlights these challenges and sets the direction for future research in the UV imaging domain. [ABSTRACT FROM AUTHOR]

Fenestration, comprising windows and other openings in a building, influences occupant health and well-being while also enhancing energy efficiency through optimized design and placement. Selecting glazing materials that block or filter harmful UV radiation is crucial, as is having reliable methods to measure their UV transmission. This research paper, which comprises Part I of II, conducts an exhaustive analysis of the predominant methodologies and associated challenges inherent in assessing ultraviolet (UV) radiation transmission and deterioration status in fenestration, with particular emphasis on the usage of spectrophotometers and radiometers. It details the economic and operational challenges associated with these instruments and the limitations they pose in terms of sample size and the impact of glazing material degradation over time. Additionally, the paper provides a comparative analysis of UV radiation transmission characteristics across different glazing configurations such as green or yellow patterned glass, laminate glass, clear glass treated with UV protection film, and float glass. The review identifies laminated glass material as offering the best protection. Although spectrophotometers and radiometers offer high levels of accuracy in transmission data detection, their adoption is hindered by considerable financial and operational challenges. The paper underscores the need for developing alternative methodologies that are economically viable, operationally less complex, and are capable of overcoming the limitations of the traditional methods of detection, which will facilitate optimal fenestration configurations for UV protection and energy efficiency in buildings. The proposed Part II paper will explore UV imaging, image processing, and computer vision techniques as potential alternative approaches. [ABSTRACT FROM AUTHOR]

Heat transfer analysis was performed on typical roofing assemblies using HEAT3, a three-dimensional heat transfer analysis software. The difference in heat transferred through the roofing assemblies considered is compared between two cases-without any steel fasteners and with steel fasteners. In the latter case, the metal roofing fasteners were arranged as per Factor Mutual Global (FMG) approvals, in the field, perimeter, and corner zones of the roof. The temperature conditions used for the analysis represented summer and winter conditions for three separate Climate Zones (CZ) namely Climate Zone 2 or CZ2 represented by Orlando, FL; CZ3 represented by Atlanta, GA; and CZ6 zone represented by St. Paul, MN. In all the climatic conditions, higher energy transfer was observed with increase in the number of metal fasteners attributed to high thermal conductivity of metals as compared to the insulation and other materials used in the roofing assembly. This difference in heat loss was also quantified in the form of percentage change in the overall or effective insulation of the roofing assembly for better understanding of the practical aspects. Besides, a comparison of 2D heat transfer analysis (using THERM software) and 3D analysis using HEAT3 is also discussed proving the relevance of 3D over 2D heat transfer analysis. [ABSTRACT FROM AUTHOR]

In recent years, a new generation of power grid system, referred to as the Smart Grid, with an aim of managing electricity demand in a sustainable, reliable, and economical manner has emerged. With greater knowledge of operational characteristics of individual appliances, necessary automation control strategies can be developed in the Smart Grid to operate appliances in an efficient manner. This paper provides a way of classifying different operational cycles of a household appliance by introducing an unsupervised learning algorithm called k-means clustering. An intrinsic method known as silhouette coefficient was used to measure the classification quality. An identification process is also discussed in this paper to help users identify the operation mode each types of operation cycle stands for. A case study using a typical household refrigerator is presented to validate the proposed method. Results show that the proposed the classification and identification method can partition and identify different operation cycles adequately. Classification of operation cycles for such appliances is beneficial for Smart Grid as it provides a clear and convincing understanding of the operation modes for effective power management. [ABSTRACT FROM AUTHOR]

In order to achieve a material and energy balance in buildings that is sustainable in the long run, there is an urgent need to assess the renewable and non-renewable resources used in the manufacturing process and to progressively replace non-renewable resources with renewables. Such progressive disinvestment in the non-renewable resources that may be substituted with renewable resources is referred to as "Renewable Substitutability" and if implemented, this process will lead to a paradigm shift in the way building materials are manufactured. This paper discusses the development of a Renewable Substitutability Index (RSI) that is designed to maximize the use of renewable resources in a building and quantifies the substitution process using solar emergy (i.e., the solar equivalent joules required for any item). The RSI of a building or a building component, i.e., floor or wall systems, etc., is the ratio of the renewable resources used during construction, including replacement and maintenance, to the building's maximum renewable emergy potential. RSI values range between 0 and 1.0. A higher RSI achieves a low-energy building strategy promoting a higher order of sustainability by optimizing the use of renewables over a building's lifetime from formation-extraction-manufacturing to maintenance, operation, demolition, and recycle. [ABSTRACT FROM AUTHOR]

Global warming is expected to increase 1.5 °C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, university campuses need to prepare to mitigate risks with building energy forecasting models. Although many scholars have developed buildings energy models (BEMs), only a few have focused on the interpretation of the meaning of BEM, including climate change and its impacts. Additionally, despite several review papers on BEMs, there is no comprehensive guideline indicating which variables are appropriate to use to explain building energy consumption. This study developed building energy prediction models by using statistical analysis: multivariate regression models, multiple linear regression (MLR) models, and relative importance analysis. The outputs are electricity (ELC) and steam (STM) consumption. The independent variables used as inputs are building characteristics, temporal variables, and meteorological variables. Results showed that categorizing the campus buildings by building type is critical, and the equipment power density is the most important factor for ELC consumption, while the heating degree is the most critical factor for STM consumption. The laboratory building type is the most STM-consumed building type, so it needs to be monitored closely. The prediction models give an insight into which building factors remain essential and applicable to campus building policy and campus action plans. Increasing STM is to raise awareness of the severity of climate change through future weather scenarios. [ABSTRACT FROM AUTHOR]

Exposures to air pollutants have been associated with various acute respiratory diseases and detrimental human health. Analysis and further interpretation of air pollutant patterns are correspondingly important as monitoring them. In the present study, the 24-h and four-month indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were measured simultaneously for a laboratory in Gainesville city, Florida. The indoor PM2.5, PM10, and NO2 concentrations were predicted using multiple linear regression (MLR), time series regression (TSR), and artificial neural networks (ANN) models. The modeling conducted in this study aims to perform a cross comparison study between these models in a symmetric environment. The value of root-mean-square error was improved by 18.33% in comparison with the MLR model. In addition, the value of the coefficient of determination was improved by 24.68%. The ANN model had the best performance and could predict the target air pollutants at 10-min intervals of the studied building with 90% accuracy levels. The TSR model showed slightly better performance compared to the MLR model. These results can be accordingly referred for studies analyzing indoor air quality in similar building types and climate zones. [ABSTRACT FROM AUTHOR]

The 24 h and 14-day relationship between indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were assessed for an elementary school (site 1), a laboratory (site 2), and a residential unit (site 3) in Gainesville city, Florida. The primary aim of this study was to introduce a biplot-based PCA approach to visualize and validate the correlation among indoor and outdoor air quality data. The Spearman coefficients showed a stronger correlation among these target environmental measurements on site 1 and site 2, while it showed a weaker correlation on site 3. The biplot-based PCA regression performed higher dependency for site 1 and site 2 (p < 0.001) when compared to the correlation values and showed a lower dependency for site 3. The results displayed a mismatch between the biplot-based PCA and correlation analysis for site 3. The method utilized in this paper can be implemented in studies and analyzes high volumes of multiple building environmental measurements along with optimized visualization. [ABSTRACT FROM AUTHOR]

At a global level, the need for energy efficiency and an increased share of renewable energy sources is evident, as is the crucial role of cities due to the rapid urbanization rate. As a consequence of this, the research work related to Positive Energy Districts (PED) has accelerated in recent years. A common shared definition, as well as technological approaches or methodological issues related to PEDs are still unclear in this development and a global scientific discussion is needed. The International Energy Agency's Energy in Buildings and Communities Programme (IEA EBC) Annex 83 is the main platform for this international scientific debate and research. This paper describes the challenges of PEDs and the issues that are open for discussions and how the Annex 83 is planned and organized to facilitate this and to actively steer the development of PEDs major leaps forward. The main topics of discussion in the PED context are the role and importance of definitions of PEDs, virtual and geographical boundaries in PEDs, the role of different stakeholders, evaluation approaches, and the learnings of realized PED projects. [ABSTRACT FROM AUTHOR]

Increasing concern over higher frequency extreme weather events is driving a push towards a more resilient built environment. In recent years there has been growing interest in understanding how to evaluate, measure, and improve building energy resilience, i.e., the ability of a building to provide energy-related services in the event of a local or regional power outage. In addition to human health and safety, many stakeholders are keenly interested in the ability of a building to allow continuity of operations and minimize business disruption. Office buildings are subject to significant economic losses when building operations are disrupted due to a power outage. We propose "occupant hours lost" (OHL) as a means to measure the business productivity lost as the result of a power outage in office buildings. OHL is determined based on indoor conditions in each space for each hour during a power outage, and then aggregated spatially and temporally to determine the whole building OHL. We used quasi-Monte Carlo parametric energy simulations to demonstrate how the OHL metric varies due to different building characteristics across different climate zones and seasons. The simulation dataset was then used to develop simple regression models for assessing the impact of ten key building characteristics on OHL. The most impactful were window-to-wall ratio and window characteristics. The regression models show promise as a simple means to assess and screen for resilience using basic building characteristics, especially for non-critical facilities where it may not be viable to conduct detailed engineering analysis. [ABSTRACT FROM AUTHOR]

Urban energy mapping plays a crucial role in benchmarking the energy performance of buildings for many stakeholders. This study examined a set of buildings in the city of Borlänge, Sweden, owned by the municipality. The aim was to present a digital spatial map of both electricity use and district heating demand in the spatial–temporal dimension. A toolkit for top-down data processing and analysis was considered based on the energy performance database of municipality-owned buildings. The data were initially cleaned, transformed and geocoded using custom scripts and an application program interface (API) for OpenStreetMap and Google Maps. The dataset consisted of 228 and 105 geocoded addresses for, respectively, electricity and district heating monthly consumption for the year 2018. A number of extra parameters were manually incorporated to this data, i.e., the total floor area, the building year of construction and occupancy ratio. The electricity use and heating demand in the building samples were about 24.47 kWh/m2 and 268.78 kWh/m2, respectively, for which great potential for saving heating energy was observed. Compared to the electricity use, the district heating showed a more homogenous pattern following the changes of the seasons. The digital mapping revealed a spatial representation of identifiable hotspots for electricity uses in high-occupancy/density areas and for district heating needs in districts with buildings mostly constructed before 1980. These results provide a comprehensive means of understanding the existing energy distributions for stakeholders and energy advisors. They also facilitate strategy geared towards future energy planning in the city, such as energy benchmarking policies. [ABSTRACT FROM AUTHOR]

Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy–efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent–based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in–depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human–in–the–loop system optimization. [ABSTRACT FROM AUTHOR]

Deteriorating levels of indoor air quality is a prominent environmental issue that results in long-lasting harmful effects on human health and wellbeing. A concurrent multi-parameter monitoring approach accounting for most crucial indoor pollutants is critical and essential. The challenges faced by existing conventional equipment in measuring multiple real-time pollutant concentrations include high cost, limited deployability, and detectability of only select pollutants. The aim of this paper is to present a comprehensive indoor air quality monitoring system using a low-cost Raspberry Pi-based air quality sensor module. The custom-built system measures 10 indoor environmental conditions including pollutants: temperature, relative humidity, Particulate Matter (PM)2.5, PM10, Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs). A residential unit and an educational office building was selected and monitored over a span of seven days. The recorded mean PM2.5, and PM10 concentrations were significantly higher in the residential unit compared to the office building. The mean NO2, SO2, and TVOC concentrations were comparatively similar for both locations. Spearman rank-order analysis displayed a strong correlation between particulate matter and SO2 for both residential unit and the office building while the latter depicted strong temperature and humidity correlation with O3, SO2, PM2.5, and PM10 when compared to the former. [ABSTRACT FROM AUTHOR]

Clay brick is a commonly used building material in China. Due to the enormous land destruction and excessive consumption of resources, such as materials and energy in the manufacture of clay brick, it is important to study its overall sustainability, i.e., in terms of impact on the environment, services, and economy. In this study, emergy analysis is employed, which offers a holistic perspective, unlike typical environmental studies. A series of emergy indices such as renewability rate (R%), nonrenewability rate (N%), unit emergy values (UEVs), emergy yield ratio (EYR), environmental loading ratio (ELR), and emergy sustainability index (ESI) were used to study manufacturing of clay brick. In addition to calculating UEVs of clay brick manufacturing in China (7.18 × 1012 sej/kg), our detailed analysis shows that the nonrenewable resources and imported energy have a dominant impact on the emergy contribution (50.6%) and within the nonrenewable resources, clay is the foremost item, accounting for 33.5% of local emergy inputs. Given different electricity UEVs, the change ranges of clay brick system UEVs are 14.9% (scenario 1), 7.24% (scenario 2), 8.91% (scenario 3), and 6.94% (scenario 4). Furthermore, several policy suggestions are discussed for improving the sustainability of the evaluated system, involving the energy structure adjustment, recycling material replacement, and promotion of energy-saving systems. [ABSTRACT FROM AUTHOR]

The existence of indoor air pollutants—such as ozone, carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen dioxide, particulate matter, and total volatile organic compounds—is evidently a critical issue for human health. Over the past decade, various international agencies have continually refined and updated the quantitative air quality guidelines and standards in order to meet the requirements for indoor air quality management. This paper first provides a systematic review of the existing air quality guidelines and standards implemented by different agencies, which include the Ambient Air Quality Standards (NAAQS); the World Health Organization (WHO); the Occupational Safety and Health Administration (OSHA); the American Conference of Governmental Industrial Hygienists (ACGIH); the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE); the National Institute for Occupational Safety and Health (NIOSH); and the California ambient air quality standards (CAAQS). It then adds to this by providing a state-of-art review of the existing low-cost air quality sensor (LCAQS) technologies, and analyzes the corresponding specifications, such as the typical detection range, measurement tolerance or repeatability, data resolution, response time, supply current, and market price. Finally, it briefly reviews a sequence (array) of field measurement studies, which focuses on the technical measurement characteristics and their data analysis approaches. [ABSTRACT FROM AUTHOR]

The Chinese cement industry produced 2150 million metric tons of cement in 2014, accounting for 58.1% of the world's total. This industry has a hugely destructive effect on the environment owing to its pollution. The environmental impact of cement manufacturing is a major concern for China. Although researchers have attempted to estimate impacts using life cycle assessment approaches, it lacks the ability to provide a holistic evaluation of the impacts on the environment. Emergy analysis, through ecological accounting, offers environmental decision making using elaborate book keeping. In spite of the high environmental impact of the cement industry, there has only been a handful of research work done to compute the unit emergy values (UEVs) of cement manufacturing in China. A thorough study of existing UEVs of cement manufacturing in China showed pitfalls that may lead to inaccurate estimations if used in emergy analysis. There is a strong need for a new, updated UEV for cement manufacturing in China, particularly reflecting both the dry and wet raw materials in the manufacturing process. This paper develops a methodology to calculate the nonrenewable resources used in cement manufacturing, particularly using mainstream cement production line. Our systematic approach-based UEV estimates of cement manufacturing in China using the quota method are 2.56 × 1012 sej/kg (wet material) and 2.46 × 1012 sej/kg (dry material). Emergy indicators such as environmental loading ratios which were calculated at 2390 (wet material) and 2300 (dry material); emergy yield ratios at 15.7 and 15.8; and emergy sustainability indices at 0.0066 and 0.0069 for dry and wet materials used in cement manufacturing, respectively; these show the immense impact on the environment in China. [ABSTRACT FROM AUTHOR]

Building energy simulation programs are used for optimal sizing of building systems to reduce excessive energy wastage. Such programs employ thermo-dynamic algorithms to estimate every aspect of the target building with a certain level of accuracy. Currently, almost all building simulation tools capture static features of a building including the envelope, geometry, and Heating, Ventilation, and Air Conditioning (HVAC) systems, etc. However, building performance also relies on dynamic features such as occupants' interactions with the building. Such interactions have not been fully implemented in building energy simulation tools, which potentially influences the comprehensiveness and accuracy of estimations. This paper discusses an information exchange mechanism via coupling of EnergyPlus™, a building energy simulation engine and PMFServ, an occupant behavior modeling tool, to alleviate this issue. The simulation process is conducted in Building Controls Virtual Testbed (BCVTB), a virtual simulation coupling tool that connects the two separate simulation engines on a time-step basis. This approach adds a critical dimension to the traditional building energy simulation programs to seamlessly integrate occupants' interactions with building components to improve the modeling capability, thereby improving building performance evaluation. The results analysis of this paper reveals a need to consider metrics that measure different types of comfort for building occupants. [ABSTRACT FROM AUTHOR]

In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041). [ABSTRACT FROM AUTHOR]