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With the advent of flexible working arrangements, we are observing a dramatic shift in how buildings are occupied today, which presents an opportunity to optimize Heating, Ventilation, and Air Conditioning system temperature setpoints based on variations in occupancy. Guidelines often suggest the adoption of the highest or lowest setpoint or setback to minimize energy consumption in hot or cold climates, respectively. However, at outdoor temperatures where variations in occupancy heat loads prompt buildings to fluctuate across cooling, free-running, and heating mode, optimal setpoints and setbacks are not always the lowest or highest. In addition, the perturbations caused by rapid switching between setpoint and setback could diminish energy savings due to system destabilization. This paper aims to systematically compare the potential energy savings from fixed and optimal setpoints and setbacks across wide-ranging occupancy scenarios (four occupancy rates and 14 patterns). Energy simulations were conducted using the Department of Energy reference models for small, medium, and large office buildings to enable an exhaustive search of optimal setpoint/setbacks in 17 climate zones. Explored setpoints were 19.5 °C to 25.5 °C with intervals of 1 °C, and setbacks were 17 °C/19 °C for heating and 26 °C/28 °C for cooling. The findings indicate that, on average, while lower occupancy heat loads results in 5.48% energy reduction, a conventional fixed setpoint and setback strategy provides an additional 11.80%, and optimal selection of setpoints and setbacks could provide an additional 34.36–38.08%, emphasizing the untapped potential energy saving. To facilitate practical applications, this paper presents an interactive graphical interface: Optimal Temperature Setpoint Tool. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency in commercial buildings is crucial for reducing energy consumption. Achieving this goal requires careful monitoring and analysis of the energy usage patterns exhibited by different devices. Nonetheless, gathering data from individual appliances in commercial buildings presents difficulties due to the large number of appliances, complex installations, and costs. This paper presents the Circuits-Level Electrical Measurements Dataset (CLEMD). The measurement was conducted at the main switchboard to a set of distribution boards instead of measuring at the individual loads. The data is gathered from an institutional setting. It consists of 42 records of vital electrical parameters including voltage, current, frequency, real power, reactive power, apparent power, power factor, and odd harmonics for electrical currents. The device deployed in the measurement were industry-grade and had a high sampling rate of 200 kHz. The measurements were done over a 40-day period, from September 16 2023 to October 25 2023. CLEMD is the first Malaysian public dataset on circuit-level electricity consumption and offers analysis opportunities in different research areas such as electricity load disaggregation at circuit level, circuit identification, load profile forecasting, and pattern recognition. [ABSTRACT FROM AUTHOR]

Small and medium commercial buildings account for nearly half of the energy consumed by commercial buildings in the United States. While energy retrofits can significantly reduce building energy consumption, buildings' owners often lack the capital and experience to perform detailed energy audits and retrofit assessments. The purpose of this paper is to introduce a low-investment, bottom-up and simplified methodology for identifying energy retrofit opportunities that benefit the owners of small and medium sized office buildings In particular, the paper addresses small and medium commercial buildings on a university campus as a proof-of-concept for other owner-operators that have small and medium commercial facilities in their portfolio. The methodology consists of an eight-step framework using publicly-available and simplified tools. While energy audits and retrofit opportunity assessments are not new, a low-cost methodology for owner-operators of small and medium commercial buildings to analyze energy consumption and identify retrofit opportunities represents a contribution to knowledge. A medium office building on a university campus in Arizona served as a case study to validate the methodology. The case study showed a maximum potential energy reduction of an estimated 50%, but the figure varies based on the types of retrofit (deep versus light), energy conservation measures selected and implemented, invested resources, and interactive effects between measures. This methodology is extensible to other owner-operators that have building utility data and would like to perform retrofit opportunity assessments themselves. [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]

Purpose: This paper aims to illustrate the extensive benefits of qualitative data analysis as a rarely undertaken process in post-occupancy evaluation surveys. As a result, there is limited evidence of what occupants say about their buildings, especially for operational parameters, as opposed to how they rate them. While quantitative analyses provide useful information on how workers feel about workplace operational factors, qualitative analyses provide richer information on what aspects of the workplace workers identify as influential to their comfort, well-being and productivity. Design/methodology/approach: The authors analysed 6,938 comments from office buildings worldwide on workers' perception of workplace operational factors: design, storage, needs, space at desks and storage in their work environments. These factors were analysed based on the buildings' design intent and use, and the associated comments were coded into positive, negative and balanced comments. The authors used a combination of coding, descriptive analysis, content analysis and word cloud to dissect the comments. Findings: The findings showed that whereas workers rated these operational factors favourably, there were significantly more negative comments about each factor. Also, the Chi-square test showed a significant association (p < 0.01) between the satisfaction scale and the type of comments received for all the operational factors. This means that when a factor is rated high in the satisfaction score (5–7), there were fewer negative and more positive comments and vice versa. The word cloud analysis highlighted vital aspects of the office environment the workers mostly commented on, such as open plan design, natural lighting, space and windows, toilets, facilities, kitchens, meeting room booking systems, storage and furniture. Research limitations/implications: This study highlights the importance of dissecting building occupants' comments as integral to building performance monitoring and measurement. These emphasise the richness and value of respondents' comments and the importance of critically analysing them. A limitation is that only 6,938 comments were viable for analysis because most comments were either incomplete with no meaning or were not provided. This underlines the importance of encouraging respondents to comment and express their feelings in questionnaire surveys. Also, the building use studies questionnaire data set presents extensive opportunities for further analyses of interrelationships between demographics, building characteristics and environmental and operational factors. Practical implications: The findings from this study can be applied to future projects and facility management to maintain and improve office buildings throughout their life cycle. Also, these findings are essential in predicting the requirements of future workplaces for robust workplace designs and management. Originality/value: The authors identified specific comments on the performance of workplaces across the globe, showing similarities and differences between sustainable, conventional, commercial and institutional buildings. Specifically, the analysis showed that office workers' comments do not always corroborate the ratings they give their buildings. There was a significantly higher percentage of negative comments than positive comments despite the high satisfaction scores of the operational factors. [ABSTRACT FROM AUTHOR]

Urban building energy modeling (UBEM) is a practical approach in large-scale building energy modeling for stakeholders in the energy industry to predict energy use in the building sector under different design and retrofit scenarios. UBEM is a relatively new large-scale building energy modeling (BEM) approach which raises different challenges and requires more in-depth study to facilitate its application. This paper performs a systematic literature review on physics-based modeling techniques, focusing on assessing energy conservation measures. Different UBEM case studies are examined based on the number and type of buildings, building systems, occupancy schedule modeling, archetype development, weather data type, and model calibration methods. Outcomes show that the existing tools and techniques can successfully simulate and assess different energy conservation measures for a large number of buildings. It is also concluded that standard UBEM data acquisition and model development, high-resolution energy use data for calibration, and open-access data, especially in heating and cooling systems and occupancy schedules, are among the biggest challenges in UBEM adoption. UBEM research studies focused on developing auto-calibration routines, adding feedback loops for real-time updates, future climate data, and sensitivity analysis on the most impactful modeling inputs should be prioritized for future research. [ABSTRACT FROM AUTHOR]

This paper investigates biomass and solar energy's present and future perspectives in low/zero energy and carbon emissions. Its data source is published articles indexed in the Scopus database. By analyzing the articles extracted in Vos viewer software, four main areas of research are found: sustainable development, economic and managerial issues, methods, algorithms, modeling technologies, and renewable energy and its sources and types. In all four sections, research gaps were observed in the field of the third generation of photovoltaics (semi-transparent solar cells)organic)) and algae. As part of the study, advanced bibliometric analysis was carried out by VOS viewer software, and 34129 articles were examined from Scopus, alongside a patent analysis using Google patents, in addition to the bibliometric analysis. It has been shown by machine learning that about 9% of future articles in all energy fields will consist of building articles, and a quarter of these articles will be in the field of renewable energy. While residential and commercial sectors are the dominant areas of renewable energy utilization and commercialization research, the potential of new generations of renewable energy technologies will create significant opportunities to achieve low/zero energy-carbon emission buildings. The paper concludes by predicting the increasing rate of renewable energy and building articles compared to energy articles by 2030, emphasizing the critical role of research in advancing sustainable energy solutions. This data mining analysis helps to identify the current gaps and opportunities. Therefore, great potential will be created to develop and commercialize a new generation of technologies in this industry. [ABSTRACT FROM AUTHOR]

This paper explores the potential of rooftop solar PV to meet the electricity demand in the urban areas of Abha city, Saudi Arabia (KSA), minimising imports from the grid. A localised energy system for Abha is proposed that considers two types of loads: (i) residential loads with a monthly aggregated energy consumption of 172,440 MWh and an electric demand of 239.5 MW, and (ii) commercial loads with a monthly aggregated energy consumption of 179,280 MWh and an electric demand of 249 MW. The grid currently supplies this load. This paper proposes a PV development planning tool for residential and commercial areas to calculate the total PV production for each type of load to achieve a balanced energy area, considering (i) the number of buildings, (ii) the type of load, (iii) the peak load, and (iv) the total PV array area in m2 per building. The results of the modelling study using real data demonstrate that the anticipated total PV production in residential and commercial areas is sufficient to meet local peak demand, and there is an excess of power that can either be stored locally or exported to the grid. [ABSTRACT FROM AUTHOR]

Rising fossil energy prices and the significantly decreasing prices of energy technology have resulted in electricity consumers having the option to install solar PV rooftops to rely on the self-consumption of clean energy. However, the increase in this amount is affecting the revenue of electricity as a utility, which must adapt and develop its business model to accommodate the situation. If the utility cannot be adapted in time, it may lead to a loss of income from services and the sale of electricity from fossil energy. The utility in Thailand's electricity market that acts as the distribution system operator (DSO) is known as the Provincial Electricity Authority (PEA), and the Metropolitan Electricity Authority (MEA) is responsible for managing distribution networks and customers. There are four types of solar rooftop-as-a-service (RaaS) business perspectives they could consider as opportunities through which to minimize revenue impact. The business services were designed for the DSO customer as follows: Consulting, Design, and Installation (CDI); Operation and Maintenance (O&M); Energy Service Company (ESCO); and Power Purchase Agreement (PPA). The model comprises four customer segments: residential buildings and small-, medium-, and large-scale commercial buildings. This paper applies SWOT, Five Forces, 4P marketing, and economic impact analyses to identify the possibilities when using the DSO business model. The SWOT analysis demonstrates that ESCO and PPA are strengths in the DSO's performance characteristics and existing customer data. In the electricity industry, both models offer enormous customer bargaining power in terms of a Five Forces analysis. The main reason is that there is currently high competition in the installation service. In the 4P analysis result, the price per unit is found to be significantly lower than in residential scenarios. Therefore, there is a format for presenting promotions with an advantage over competitors. Deploying an after-sales service that brings convenience to all customer segments is needed. The economic analysis conducted using Cournot competition game theory shows a significant differential in the Medium (M) and Large (L) customer sectors' competition due to lower technology prices. In conclusion, with the current regulatory framework and criteria, the ESCO and PPA show the best practical model from a utility business perspective. The recommendation for DSO is to create a strategic ecosystem and to link it with private companies as their partnership business. [ABSTRACT FROM AUTHOR]

When it comes to smart cities, one of the biggest issues is energy optimization. This is because these cities employ a large number of interconnected devices to autonomously manage city operations, which consumes a lot of energy. This difficulty has been addressed in this paper by using the advantages of contemporary cutting-edge technologies such as the Internet of Things (IoT), 5 G, and cloud computing for energy efficiency in smart cities. With the use of these cutting-edge technologies, we have proposed a model that can be used to optimize energy consumption in smart homes and smart cities alike. Street lighting, building and street billboards, smart homes, and smart parking are among the four essential features of smart cities that would benefit from the proposed model's energy savings. All smart city electric appliances will be equipped with IoT sensors that will detect movements and react to commands. In order to transport data swiftly between communication channels and the cloud, 5 G technology will be deployed, and the cloud technology will be used to store and retrieve data effectively. The suggested model was evaluated using mathematical modeling, and the findings indicate that the proposed model may assist in improving energy usage in smart cities. [ABSTRACT FROM AUTHOR]

The change over time of energy performance of large chillers used in commercial and institutional buildings is seldom presented in the literature. Most studies are based on measurements of small capacities of residential air-conditioners and heat pumps. Authors found in the literature only three papers on this topic. This paper presents the evidence-based assessment of long-term performance of two identical large capacity chillers that serve the cooling system of a university campus. The paper presents the estimation of uncertainty of direct and derived measurements by using as example measurements recorded at 15 min time intervals by the building automation system during the summer of 2016. First, the paper verifies the hypothesis that weekly values of coefficient of performance (COP) of these large chillers do not change significantly over the summer of 2016. Second, the t-test also verifies the hypothesis of no significant change of seasonal COP values from 2009 to 2017. Third, a linear correlation-based model shows a variation of COP values by 1-2% per year between 2009 and 2017. The results show that the seasonal variation of COP is not statistically significant. [ABSTRACT FROM AUTHOR]

Summary: Power systems face more uncertainty by increasing photovoltaic system installations on the roof of buildings. To optimally manage energy and available flexibility in a building, stochastic optimization is used to take an optimal decision under uncertainty and minimize the operational cost. In stochastic optimization, a scenario set is used as an input to represent the uncertainty in a random variable, PV energy generation in this case. In this paper, a data‐driven method is proposed to obtain the distribution of the random variable and later generate scenario sets representing the uncertainty on day‐ahead PV energy generation installed on the roof of a building. This method is only based on historical PV generation and it does not require any other external data such as weather forecasts. A machine learning‐based technique is applied to forecast the PV energy production following by generating a scenario set for day‐ahead decision‐making. Later, the day‐ahead PV‐battery system management problem is formulated as a two‐stage stochastic optimization while the generated scenario set is the input of this optimization. The proposed algorithm is tested in the day‐ahead scheduling of a PV‐battery system for a commercial building, informed by real‐life measurement data. The results show that the proposed algorithm is able to capture the uncertainty in PV system while providing a cost‐optimal and reliable solution to the application problem. Without using any weather data, the error of the proposed PV energy forecast method reaches to NRMSE = 11.89, while there is 5% reduction in the operational cost of the proposed two‐stage stochastic optimization. Moreover, the proposed algorithm is easy to implement in the energy management system of a building to manage PV‐battery system. [ABSTRACT FROM AUTHOR]

Building energy assessment models are considered to be one of the most informative methods in building energy efficiency design, and most of the current building energy assessment models have been developed based on machine learning algorithms. Deep learning models have proved their effectiveness in fields such as image and fault detection. This paper proposes a deep learning energy assessment framework with interpretability to support building energy efficiency design. The proposed framework is validated using the Commercial Building Energy Consumption Survey dataset, and the results show that the wrapper feature selection method (Sequential Forward Generation) significantly improves the performance of deep learning and machine learning models compared with the filtered (Mutual Information) and embedded (Least Absolute Shrinkage and Selection Operator) feature selection algorithms. Moreover, the Deep Forest model has an R2 of 0.90 and outperforms the Deep Multilayer Perceptron, the Convolutional Neural Network, the Backpropagation Neural Network, and the Radial Basis Function Network in terms of prediction performance. In addition, the model interpretability results reveal how the features affect the prediction results and the contribution of the features to the energy consumption in a single building sample. This study helps building energy designers assess the energy consumption of new buildings and develop improvement measures. [ABSTRACT FROM AUTHOR]

As commonly stated, we cannot manage what we do not measure. Understanding the flow of energy and its end-uses within a building is critical for energy management. Therefore, the lack of high resolution energy submetering is a significant barrier to efficient energy management in buildings. Despite this, many buildings still lack adequate submetering for their major end-uses because of the cost and practical restrictions. Energy disaggregation techniques aim at breaking down the bulk meter energy data into primary end-uses to gain insight into consumption patterns. However, high resolution, trustworthy BAS trend data is essential to develop reliable disaggregation techniques and capture unmeasured energy flow accurately. This paper explores a time series decomposition based method to disaggregate the total energy use into three major end uses namely lighting and plug loads, cooling, and heating energy use without BAS trend data. The results were compared with actual submetered data from ten office buildings in Ottawa, Canada for validation purposes. Specific insights into lighting and thermal scheduling, as well as hourly, daily, and monthly operational variations based on the de-composition components were discussed. The promising performance of the proposed method suggests that it could be used for quick and low cost auditing of commercial buildings with access to only the building's total energy use data. [ABSTRACT FROM AUTHOR]

Building energy usage has been an important issue in recent decades, and energy prediction models are important tools for analysing this problem. This study provides a comprehensive review of building energy prediction models and uncertainties in the models. First, this paper introduces three types of prediction methods: white-box models, black-box models, and grey-box models. The principles, strengths, shortcomings, and applications of every model are discussed systematically. Second, this paper analyses prediction model uncertainties in terms of human, building, and weather factors. Finally, the research gaps in predicting building energy consumption are summarised in order to guide the optimisation of building energy prediction methods. [ABSTRACT FROM AUTHOR]

A major challenge in renewable energy planning and integration with existing systems is the management of intermittence of the resources and customer demand uncertainties that are attributed to climates. In emerging distributed grids, state-of-the-art optimization techniques were used for cost and reliability objectives. In the existing literature, power dispatch and demand side management schemes were implemented for various techno-economic objectives. In renewable energy-based distributed grids, power dispatch is strategic to system operations. However, demand side management is preferred, as it allows more options for customer participation and active management of energy in buildings. Moreover, the demand side management can simply follow supplies. This paper investigates the implications of demand side management as it affects planning and operations in renewable energy-based distributed grids. Integration of demand side management in customer-oriented plans such as the time-of-use and real-time-pricing on residential and commercial demands is conceptualised to ensure effective customer participation which maintains the valued comforts. Moreover, the optimised tariff integrated demand side management implementations based on the utility-initiated demand response programmes are envisaged to offset conflicting objectives of the economy and customer comforts within residential and commercial demands and are also viewed as a step towards efficient management of energy in buildings. [ABSTRACT FROM AUTHOR]

In response to excessive energy consumption and severe pollution, green building has gained increasing attention around the world. Governments' top-down incentive schemes and consumers' bottom-up choice preferences are two major channels of residential green building promotion. Regarding the bottom-up route, high liveability performance is critical to ensuring that occupants are willing to make secondary purchases or provide recommendations. Therefore, this paper, using post-occupancy evaluation, aims to evaluate and compare the liveability performance of green and conventional buildings from the perspectives of occupants. The results verified that the eco-label effect (i.e., subjective differences for building types) influenced the occupants' evaluations of building performance. When controlling for eco-label bias, we found that green buildings were not superior to conventional buildings in terms of liveability. This is highly relevant to evaluations of the orientation of green building certifications that concentrate on the consumption of energy and material resources but neglect the living experience of occupants. In addition, indicators related to thermal comfort (e.g., indoor temperature or frequency of air conditioner use) played an important role in the occupants' liveability evaluations. These findings provide concrete guidance regarding how the evaluation systems of green building certifications in various countries should be upgraded in the near future. [ABSTRACT FROM AUTHOR]

As the world continues in the quest to fight global warming and environmental pollution by gradually moving to renewable sources of energy, there is also a need to reduce building energy consumption by refurbishing old and historic buildings to meet the required energy standards. While this approach may differ from city to city across the globe, the refurbishment of old and historic buildings would make a significant impact. That is why it is necessary to educate building owners or occupants by simulating the existing energy consumption and proposing appropriate refurbishment strategies. Because the accuracy of energy simulation is directly proportional to the amount of data available and its reliability, there is a need to find creative ways of supplying incomplete or missing building information. The present paper describes a concept that enables individual building occupants or owners to provide this missing information. Implemented and tested with the 3D city model of Aachen, the proof-of-concept enables individual building owners or occupants to perform energy simulations based on energy information supplied. [ABSTRACT FROM AUTHOR]

Anastasi et al. [[7]] talk about how COVID-19 has led to an acceleration of the transition process of buildings into smart buildings and active environments, especially with regard to areas such as occupant density and air quality. This paper makes a lot of similar points to some aforementioned studies, particularly [[1], [3]], but the authors stressed the importance of implementing a building performance simulation as a framework for energy-efficient building development. Construction is a key sector for green growth on a global scale. Szul et al. [[5]] conducted a study on the feasibility of using Takagi-Sugeno Fuzzy Modelling to predict the energy efficiency in buildings, particularly in buildings undergoing thermal improvement. [Extracted from the article]

This paper provides an open dataset of measured energy use, solar energy production, and building air leakage data from a 328 m2 (3,531 ft2) all-electric, zero energy commercial building in Virginia, USA. Over two years of energy use data were collected at 1-hour intervals using circuit-level energy monitors. Over six years of solar energy production data were measured at 1-hour resolution by 56 microinverters (presented as daily and monthly data in this dataset). The building air leakage data was measured post-construction per ASTM-E779 Standard Test Method for Determining Air Leakage Rate by Fan Pressurization and the United States Army Corps (USACE) Building Enclosure Testing procedure; both pressurization and depressurization results are provided. The architectural and engineering (AE) documents are provided to aid researchers and practitioners in reliable modeling of building performance. The paper describes the data collection methods, cleaning, and convergence with weather data. This dataset can be employed to predict, benchmark, and calibrate operational outcomes in zero energy commercial buildings. Measurement(s) measured energy use • solar energy production • building air leakage Technology Type(s) circuit-level energy monitor • microinverter • ASTM-E779 Standard Test Method for Determining Air Leakage Rate by Fan Pressurization Factor Type(s) temporal interval Sample Characteristic - Environment zero energy commercial building Sample Characteristic - Location Commonwealth of Virginia Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16869407 [ABSTRACT FROM AUTHOR]

The main purpose of energy benchmarking in commercial buildings is to put actual energy consumption into perspective so that building owners and occupiers can take steps to reduce it. Many rating tools, such as the display energy certificate in the UK, are primarily based on floor area. This means that nearly empty buildings can receive a good rating while intensively utilised buildings are penalised, even though their energy use per person may be much lower. To provide more meaningful benchmarking requires consideration of the intensity of use. This paper proposes a simple approach to include occupancy density and hours of use in the benchmarking methodology for commercial buildings. The paper also discusses how landlord and tenant energy can be benchmarked separately to reflect who is responsible for the energy consumption. Finally, the paper suggests modifying the standard A to G rating scale used in the UK to better reflect the diversity of energy performance in commercial buildings. [ABSTRACT FROM AUTHOR]

The rising cost and demand for energy have prompted the need to devise innovative methods for energy monitoring, control, and conservation. In addition, statistics show that 20% of energy losses are due to the mismanagement of energy. Therefore, the utilization of energy management can make a substantial contribution to reducing the unnecessary usage of energy consumption. In line with that, the intelligent control and optimization of energy management systems integrated with renewable energy resources and energy storage systems are required to increase building energy efficiency while considering the reduction in the cost of energy bills, dependability of the grid, and mitigating carbon emissions. Even though a variety of optimization and control tactics are being utilized to reduce energy consumption in buildings nowadays, several issues remain unsolved. Therefore, this paper presents a critical review of energy management in commercial buildings and a comparative discussion to improve building energy efficiency using both active and passive solutions, which could lead to net-zero energy buildings. This work also explores different optimum energy management controller objectives and constraints concerning user comfort, energy policy, data privacy, and security. In addition, the review depicts prospective future trends and issues for developing an effective building energy management system, which may play an unavoidable part in fulfilling the United Nations Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Occupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy simulation engineers to understand how an entire building's energy consumption varies with different occupancy levels in the process of building automation systems or in assessments of building performance with benchmarking lines. Because commercial buildings usually have large scales, complex layouts and a large number of people, it is a challenge to simulate the relationships between an entire building's energy consumption and occupancy. This study proposes a fast method for calculating the influence of occupancy on the energy consumption of commercial buildings with different building layouts and existing occupancies. Other occupant behaviors, such as the opening of windows and adjustment of shading devices, are comprehensively reflected in two basic building parameters: the balance point temperature and the total heat transmission coefficient of the building. This new method can be easily used to analyze how building energy varies with occupancy without a physical building's energy model. An office building in Shanghai is taken as a case study to validate the proposed method. The results show that the coefficient of determination R2 between the calculated value and actual value is 0.86, 0.8 and 0.71 for lighting, cooling and heating energy, respectively, which is suitable in engineering applications. [ABSTRACT FROM AUTHOR]

Based on the emission factor method and LMDI-LEAP model, this paper systematically studies the current situation, influencing factors and changing trend of carbon emissions from rural buildings in a typical village located in southern China. The results showed that (1) the per capita carbon emissions generated by the energy consumption of rural buildings is 2.58 tCO2/a. Carbon emissions from electricity consumption in buildings account for about 96.07%; (2) the per capita building area, building area energy intensity, population size, population structure and carbon emission coefficient affect rural building carbon emissions, with contribution rates of 70.13%, 31.27%, 0.61%, −1.21% and −0.80%, respectively; (3) from 2021 to 2060, the carbon emissions of rural buildings are expected to increase first and then decrease. In 2021, the base year, carbon emissions from buildings were 2755.49 tCO2. The carbon emissions will peak at 5275.5 tCO2. Measures such as controlling the scale of buildings and improving the utilization rate of clean energy can effectively reduce carbon emissions, in which case the peak can be reduced to 4830.06 tCO2. Finally, the countermeasures and suggestions about rural building energy saving and emission reduction are proposed, including improving the construction management, raising energy efficiency standards in buildings, increasing the proportion of clean energy and raising residents' awareness of energy conservation. [ABSTRACT FROM AUTHOR]

The current +−0.5 PMV (Predicted Mean Vote) targets adopted by NABERS (National Australian Built Environment Rating System) is the practical range deemed acceptable for 90% acceptability for commercial buildings in Australia, however thermal comfort satisfaction scores measured in office buildings still show high percentages of dissatisfied occupants. This paper aims to demonstrate the potential of curbing energy consumption from commercial buildings in Australia by increasing summer temperature set-points. A 10-year NABERS dataset, along with objective and subjective thermal comfort and air quality data from NABERS-certified offices are investigated in this study. Furthermore, different simulation scenarios are tested to investigate the discomfort hours and energy consumption for various summer temperature setpoints. Result analysis shows that occupants' satisfaction in NABERS-certified buildings was not within the 90% satisfaction, with being too cold/hot as the main source of dissatisfaction. Objective measurements also showed temperature was out of recommended range for several datapoints. Simulation results indicate that, within the average range of 21–24.9 °C, there is not a significant difference in discomfort hours that could drive the selection of one temperature set-point over the other. Challenging the current practices, results suggest that a cooling set point temperature on the upper limit of the range indicated by the Australian standard AS 1837–1976 may minimize the energy consumption without significantly increasing discomfort, or even increasing the perceived satisfaction with the indoor environment. [ABSTRACT FROM AUTHOR]

This paper examines the issue of strategic planning of fuel supplies in combined heat and power systems. This is a major challenge in energy modeling because heating-degree day calculation methods only address short-term horizons and are not suitable for the long-term planning of fuel supplies. In this work, a comprehensive method is proposed for strategic fuel supply planning of independent heat producers. The method considers changes in the market dynamics of residential and commercial properties, the annual rate of customer acquisition by the network operator, customer disconnections, as well as the thermal modernization of buildings for estimating the long-term thermal energy demand of an urban area. Moreover, the method develops a mathematical model to minimize production costs, taking into account the technical constraints of the system. The proposed strategic planning tool, in addition to information on the quantities of fuel consumed for heat and electricity production, also provides valuable management information on the operational costs of the CHP system and its environmental impact. The application of the method is illustrated with the analysis of a large-scale combined heat and power plant supplying heat and electricity to a city with over 500,000 inhabitants. The results indicate that depending on the changes in the primary and secondary heat markets, the demand for energy carriers may range from 107.37 TWh to 119.87 TWh. [ABSTRACT FROM AUTHOR]

Controlling solar gain is essential to reduce the energy consumption by residential and commercial buildings, which are responsible for ≈40% of all energy generated globally. However, the dynamic control of solar gain in the near‐infrared that corresponds to ≈50% of the overall Sun's energy at the Earth's surface, separately from the visible spectral range has been a challenge, albeit commercial coatings provide passive control while retaining highly visible transmission. To address this problem, in this paper switchable silver colloids (SSCs) are demonstrated, colloidal analogs of the common silver coatings used for solar gain control. When dispersed in a nematic liquid crystal, orientations of such plasmonic silver nanoplates are controlled by ≈1 V low‐voltage electric fields, allowing for sub‐second switching of the near‐infrared‐based solar gain. Installed and retrofit products made from thin films of such nanoplate dispersions confined between glass and plastic substrates, respectively, exhibit electrically controlled infrared‐based solar gain while retaining high visible‐range transparency, low haze, and high color rendering index. This study's findings reveal a great potential of soft‐matter systems in addressing energy‐related problems. [ABSTRACT FROM AUTHOR]

The purpose of this research is to answer the question, 'can analytics software measure end user computing electricity consumption?' The rationale being that the success of traditional methodologies, such as watt metres, is limited by newly evolved barriers such as mobility and scale (Greenblatt et al., in Field data collection of miscellaneous electrical loads in Northern California: initial results. Ernest Orlando Lawrence Berkeley National Laboratory research paper, pp 4–5, 2013). Such limitations significantly reduce the availability of end user computing use phase energy consumption field data (Karpagam and Yung, in J Clean Prod 156:828, 2017). This causes computer manufacturers to instead rely upon no-user present energy efficiency benchmarks (Energy Star, in Product finder, product, certified computers, results. Washington, D.C.: United States Department of Energy. https://www.energystar.gov/productfinder/product/certified-computers/results, 2021) to act as baseline data for product carbon footprint reports. As the benchmark approach is previously tested to cause scope 2 greenhouse gas emissions quantification to be inaccurate by − 48% to + 107% (Sutton-Parker, in Determining end user computing device Scope 2 GHG emissions with accurate use phase energy consumption measurement, 1877-0509. Amsterdam: Science Direct, Elsevier B.V., 2020), testing a new methodology that includes the impact of human–computer interaction is arguably of value. As such, the proposed method is tested using a distributed node based analytics software to capture both computer asset and human use profile data sets from one hundred and eleven computer users operating in a subject organisation for 30-days. The simple rationale is that the node, unlike a watt metre, is not restricted by location, can be deployed and monitored globally from a centralised location and can move with the computer to ensure constant measurement. The resulting data sets are used to populate a current use phase electricity consumption calculation data flow (Kawamoto et al., in Energy 27:255, 2001; Roth et al., in Energy consumption by office and telecommunications equipment in commercial buildings: energy consumption baseline, 2002) in order to examine for omissions. Additionally, to test for data accuracy, one computer user acts as a control subject, measuring electricity consumption with both a watt-metre and the analytics software. The rationale being that the watt-metre data is extensively proven to be accurate (Energy Star, in Energy star computers final version 8.0 Specification, Washington D.C., United States Department of Energy. https://www.energystar.gov/products/spec/computers%5fversion%5f8%5f0%5fpd, 2020) and will therefore expose errors produced by the software in relation to power draw, on-time and resulting kilo-watt hours (kWh) values. Further to the data capture period, the findings are mixed. Positively, the new method overcomes the barriers of numerous, assorted devices (scale) operating in ever changing locations (mobility). This is achieved by the node reporting in real-time make and model asset data together with device specific electricity consumption and location data via internet technologies. Negatively, the control subject identifies that the electricity consumption values produced by the software are inaccurate by a relatively constant 48%. Furthermore, data omissions are experienced including the exclusion of computer displays caused by the node requiring an operating system to collect data. This latter point would exclude the energy consumption measurement and therefore concomitant greenhouse gas emissions of any displays connected to desktop or mobile computers. Consequently, whilst the research question is answered, the identification of the software exaggerating use phase energy consumption by 48% and excluding peripheral devices, determines the analytics methodology to be in need of further development. The rationale being that use phase consumption quantification is key to lifecycle assessment and greenhouse gas accounting protocol and both require high levels of accuracy (WBCSD and WRI, in The greenhouse gas protocol. A corporate accounting and reporting standard, Geneva, Switzerland and New York, USA. https://ghgprotocol.org/corporate-standard, 2004). It is therefore recommended that further research be undertaken to specifically address omissions and to reduce the over reporting aspect identified as caused by algorithms in the software used to calculate hardware power draw. [ABSTRACT FROM AUTHOR]

This document is a special edition of Science and Technology for the Built Environment (STBE) that features expanded versions of conference papers presented at the 2022 ASHRAE/Oak Ridge National Laboratory Buildings XV Conference. The conference focused on building energy efficiency and conservation, with a history dating back to 1979. The papers in this edition cover a range of topics related to energy efficiency and moisture durability challenges in building materials and systems. The document emphasizes the importance of reducing energy consumption and carbon emissions in the building sector to mitigate climate change. [Extracted from the article]

Intelligent energy saving and energy efficient technologies are a modern large-scale global trend in the development of energy systems. Accurate estimates of energy savings are important for promoting energy-efficient construction projects and demonstrating their economic potential. The growing digital measurement infrastructure used in commercial buildings increases the availability of high-frequency data that can be employed for anomaly detection, diagnostics of equipment, heating systems, and ventilation, as well as optimization of air conditioning. This implies the application of modern machine learning methods capable of generating more accurate energy consumption forecasts for buildings to improve their energy efficiency. In this paper, based on the gradient boosting model, a method for modeling and forecasting energy consumption of buildings is proposed and computer algorithms for its software implementation in the SymPy computer algebra system are developed. To assess the efficiency of the proposed algorithms, a dataset that characterizes energy consumption of 300 commercial buildings is used. Results of computer simulations show that these algorithms improve the accuracy of energy consumption forecasts in more than 80% of cases as compared to other machine learning algorithms. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources creates a challenge for the stability of current power systems due to their intermittent and stochastic nature. This paper presents the field results of an efficient demand response solution for controlling and adjusting the electric demand of buildings in an energy district through the activation of their thermal mass while respecting the occupants' thermal comfort constraints. This multilevel control approach aims to support grid flexibility during peak times by constraining the energy exchange with the grid and increasing the self-consumption of the district. The results show a great potential for increasing the self-consumption up to 37% for offices, as well as improving the indoor environment, based on real data collected from a case study in Greece. [ABSTRACT FROM AUTHOR]

With the development of economy, commercial sector building energy consumption becomes more and more noticeable in China. The aim of this paper was to study the influencing factors governing commercial sector building energy consumption and the decoupling relation between building energy consumption and commercial sector economic development. The main results of this paper are as follows: (1) Commercial sector energy intensity decreased from 61.78 gce/yuan in 1991 to 36.70 gce/yuan in 2011, representing a decrease of 40.59 %. Since 2003, electricity has exceeded coal products and become the dominating energy. (2) The economic activity effect makes a positive contributor to the increase in commercial sector building energy consumption; however, the energy intensity effect plays an important role in decreasing energy consumption, followed by the energy structure effect. Same results have also been observed according to the relational degree. (3) Only four kinds of decoupling index occurred over 1991-2011. Strong decoupling occurred only in the years 1994, 1997, and 2008. [ABSTRACT FROM AUTHOR]

Energy-efficient and sustainable technologies are necessary to lower energy and carbon footprints. Many technologies are being pursued to meet the increasing energy demand in buildings. An attractive option is efficient utilization of available energy resources, including renewables, to support current and future building energy needs while targeting grid resiliency, energy, and environmental security at an affordable cost via on-site cogeneration-based approaches. This must include energy-efficient technologies with lower greenhouse gas emissions and optimized cost, performance, and reliability. This paper presents the economic and environmental benefits associated with power technologies such as thermionics and solid oxide fuel cells. Hybrid configurations consisting of heat pumps, power systems, and renewable photovoltaics in cogeneration and trigeneration modes of operation are presented. The role of such technologies in lowering CO2 emissions while improving energy resiliency and serving the needs of underprivileged communities is discussed. The key barriers of affordability and potential solutions for large-scale implementation of these promising technologies are reviewed. Case studies demonstrating the influence of power rating, electrical efficiency, design configuration, carbon dioxide intensity of the grid, and fuel on annual greenhouse gas emissions are presented for residential and commercial buildings. [ABSTRACT FROM AUTHOR]

Purpose – This legal update examines the implications for commercial landlords of regulations required to be made under Energy Act 2011, and to come into force by 1 April 2018. Under those regulations, a landlord “may not let” commercial premises falling below a specified energy performance rating (likely to be E). The sanction of market deprivation arguably represents a significant shift in the balance between incentive and compulsion as the key policy tool adopted by the UK Government in seeking to improve the energy performance of commercial buildings. The paper aims to discuss these issues. Design/methodology/approach – The paper sets out and reflects a practitioner's concerns relating to the proposed new sanction of market deprivation. It identifies and highlights practical difficulties likely to be encountered when considering the interaction of the proposed regulations with existing statute (e.g. Landlord and Tenant Act 1954) and contractual provisions such as tenant break clauses. Findings – The prospect of being unable to let commercial premises that fall below a specified energy efficiency rating must focus landlord attention on rights to enter to carry out improvement works. The paper identifies a potentially significant trap for landlords in the model green lease clauses issued by the Better Buildings Partnership where tenant consent is required. Research limitations/implications – The paper does not reflect extensive or exhaustive academic research. Consistently with its purpose a legal update, it identifies key issues likely to be encountered by practitioners. Practical implications – The principal practical implication is the need for landlords and their professional advisors to consider as part of any current lease negotiations the need to secure rights of entry for landlords to carry out improvement works where premises are at risk of falling below the energy performance rating likely to be specified in regulations to be made under Energy Act 2011, s 49. Originality/value – The paper reflects a practitioner's views, developed through client matters and also through designing and delivering professional training sessions, on the likely implications of the requirement for regulations under Energy Act 2011. [ABSTRACT FROM AUTHOR]

Promoting net zero energy buildings (NZEB) is among the key carbon emissions reduction approaches widely adopted by policymakers in recent years in the U.S. and the EU countries (see work of Marszal ). Due to the relatively higher cost of electricity generation from renewable energy (RE), federal, state, and local governments offer various financial incentive programs to promote NZEB. This paper presents a model-based framework for the policymakers to design suitable incentive programs. The model is a mixed integer program (MIP) that is particularized for wind and solar energy sources. The MIP model finds an optimal design for a NZEB. The optimal design and the cost of NZEB is then used to design incentive programs. The incentive programs considered includes loans, production tax credit, and net metering, among others. A time-of-day pricing is assumed to be in effect. The model is implemented on commercial buildings in Tampa, Florida, U.S.A. For a given region, the framework provides policymakers two reports, 1) a set of optimal portfolios of incentives for different classes of commercial buildings (based on credit rating, expected return on investment (ROI), and building type), 2) for a specified portfolio of incentives, determines which classes of commercial buildings will be willing to invest in RE. [ABSTRACT FROM AUTHOR]

This study describes the application of multi‐criteria decision analysis (MCDA) to prioritise the seismic risk mitigation of existing buildings in earthquake‐prone Wellington, New Zealand. Through retrofitting or demolition, this is an important requirement in many cities around the Pacific Rim and in other high‐level seismic hazard locations. The prioritisation strategy proposed here, based on MCDA methods, can provide decision‐makers with a fast and reliable support tool for identifying the optimal sequencing of their retrofitting programmes. The premise of the MCDA analysis presented in this paper is that there are multiple criteria that determine societal prioritisation preferences; these are limited not just to life safety (often the explicit/exclusive priority of governments) and commercial value (the main concern of many building owners). The study demonstrates how different measures, within four general criteria—life safety and geo‐spatial, economic, and socio‐cultural roles—can be operationalised as a viable framework for establishing intervention policy priorities. [ABSTRACT FROM AUTHOR]

This paper utilizes CDEGS software to calculate the transient ground potential rise (GPR) in a commercial building, with a base of 39\ \m\ \times 13\ \m and a height of 21 m, during direct lightning strokes. The influences of soil resistivity, location of the lightning strike point, and wave shape of the lightning current on the transient GPRs of three scenarios for the building are analyzed. Such scenarios within the building include the following: 1) devices are grounded independently; 2) devices are grounded via vertical ground electrodes that are connected to each down conductor fixed on the outside of the building; and 3) devices are grounded via a ring ground electrode that is sited around the periphery of the building. The results show that the ring ground electrode provides effective lightning protection for the building under lightning and, therefore, is recommended. [ABSTRACT FROM AUTHOR]

The energy supply of buildings in urban contexts is undergoing significant changes. The increase of renewable sources for electrical and thermal energy generation will require flexible and secure supply systems. To reflect and consider these changes in energy systems and buildings, dynamic simulation is one key element. Sparse and limited access to detailed building information as well as computing time are challenges for building simulation on urban-scale. In addition, data acquisition and modelling for building performance simulation (BPS) are time-consuming and error-prone. To enable the use of BPS on urban-scale, this paper presents TEASER, an open framework for urban energy modelling of building stocks (open-source athttps://github.com/RWTH-EBC/TEASER). TEASER provides an interface for multiple data sources, data enrichment and export of ready-to-run Modelica simulation models. The paper presents TEASER's methodology and package structure. Three use cases show TEASER's capabilities on the building, neighbourhood and urban scales. [ABSTRACT FROM PUBLISHER]

Rainwater harvesting provides a potential source of supplemental water supply to meet increasing urban demand. Distributed solutions based on many small capture devices are often viewed as inefficient in terms of both cost and volume of water captured. This paper describes a framework for assessing the value of distributed rainwater capture as a substitute for a share of municipal water supplied to residential and commercial buildings. The analysis was applied to the Ballona Creek watershed in Los Angeles County, California, evaluating several cistern sizes and participation rates to estimate the amount and value of water, energy, and carbon savings offset by captured water. Results show that rainwater capture may be an efficient approach to augmenting water supply under certain conditions. From the perspective of the water supplier, capturing water for outdoor use is cost-effective, but including indoor use is not due to installation and maintenance costs. From the perspective of the homeowner, installing a cistern for irrigation would result in net cost savings over time, and under certain conditions, higher benefits are realized from larger cisterns. [ABSTRACT FROM AUTHOR]