Building User Research Articles

Sustainability and Energy Efficiency in the World Heritage Site of the Historic Centre of Mexico City

On 26 April 2017, Mexico formally adopted the 2030 Sustainable Development Agenda to link the national government to local governments, the private sector, civil society and academia. The restoration of heritage residential buildings (recovery programme) and the construction of new residential buildings aim to create a diversified environment for populations at different economic levels. However, the restoration programme faces conservation challenges. Given this context, this paper presents the results of in-depth, semi-structured interviews conducted with stakeholders in Mexico’s heritage and energy sectors. The duration of each interview was approximately two hours, with 52,372 total interview words. The paper identifies prevailing opinions regarding prioritising historical values, energy efficiency, historic buildings and users concerning Mexico City’s Historical Centre, a World Heritage Site needing a more appropriate sustainable development plan. Using grounded theory and thematic analysis, the interview data were analysed based on the interrelationship between thermal comfort, energy efficiency and heritage conservation changes over time. The results of this research will strengthen our understanding of the interventions and processes involved in managing and living in this World Heritage Site and its future impacts on buildings. The approach also underscores the importance of how stakeholders prioritise different values in making energy efficiency-promoting decisions and enhances awareness of the decision-making process and actions adopted by heritage building users. Thus, understanding the dynamic interrelationship between values, users and energy could improve the sustainable management of heritage sites and future development.

Identifying salient features of cooling energy usage of commercial buildings using explainable artificial intelligence

ABSTRACT Electrical energy usage of commercial buildings (over 60% in the USA) is of growing concern due to its impact on the environment. To reduce it, it is necessary to identify the factors contributing to commercial building energy usage. Towards this, we studied the importance of various building features and their contributions on cooling energy, using two datasets of Commercial Building Energy Consumption Survey (CBECS) 2018 and 2012. For this, Shapash, an explainable artificial intelligence technique, was used, with Random Forest, to predict the cooling energy usage intensity (EUI). The two most important features for 2018 are cooling degree days and principal building activity, while cooling degree days and cooling percentage were for 2012 with 54.79% and 33.46% contributions of the total consumption respectively. The comparison of feature importance indicated that cooling degree days are the topmost important feature impacting cooling EUI and have a more significant contribution of 34.29% for 2018 and 19.68% for 2012. Overall, the most important features impacting cooling EUI were cooling degree days, principle building activity, cooling percentage and total hours open per week. The results from this study provide insights into the most significant factors influencing energy consumption and help in developing strategies for reducing it.

Students’ Perception of the Natural Daylighting in Higher Education Design Studios

An energy-efficient and environmentally friendly building are key to establishing green buildings. Both key concepts are predominantly significant in today's sustainable development concept. Higher education institutions have started their green and sustainable campaign to meet the government's initiative to reduce energy consumption. Energy consumption is increasing annually, whereby for the past 10 years, energy usage in government buildings has increased from 50,000 GW to 109,000 GW per year. The electricity bills increased due to the extensive use of artificial light. Artificial lighting is used extensively during the daytime in higher education buildings due to poor passive daylighting strategies in many buildings. Therefore, this paper intends to present the findings of a comprehensive and systematic analysis of students’ perceptions of natural daylight in design studios in higher education buildings. Survey-based investigations were conducted to analyse the student's perception of natural daylight. 149 questionnaires were distributed to Interior Design students from higher educational institutions. The data is being analysed using statistical analysis with the use of SPSS as the tool for analysis. The research found that the respondents prefer brighter natural daylight to artificial lighting in the studio. However, their design studio receives a high glare and thermal heat. The high glare and thermal heat subsequently made the respondents feel inconvenienced and uncomfortable due to glare and heat gain. As this paper only provides students' perceptions about natural daylight in the design studio, further study is needed to identify types of daylight strategies used in the design studio.

EVALUATION OF ACCESSIBILITY OF WHEELCHAIR USERS IN THE SMITH BUILDING, ALAM SUTRA, TANGERANG CITY, INDONESIA

The existence of skyscrapers continues to increase in the city of Jakarta and its surroundings. But unfortunately, of the many buildings, only a few are friendly to persons with disabilities. This research will focus on evaluating the accessibility of wheelchair users in observation areas in multi-storey buildings, research on the accessibility of wheelchair users in multi-storey buildings using quantitative methods with a deductive verification approach. In general, the garden area of The Smith building can be accessed from the apartment area and office and soho areas. There are no accessibility problems with the layout of furniture and lighting in the observation areas of the apartments, offices and SOHOs, however, there are several obstacles in the entrance ramp area on the side North, constraints open and close the glass door (swing door type).

Thermal Comfort Assessment in University Classrooms: A Discriminant Analysis for Categorizing Individuals According to Gender and Thermal Preferences

The concern with the well-being of users in buildings has become increasingly essential, covering aspects related to health, energy efficiency, and productivity. The thermal environment evaluation in buildings has become more frequent due to the time people spend inside them. In this context, this study aimed to analyze thermal comfort in classrooms at a Brazilian University. During the autumn, 50 measurements were performed, resulting in 519 valid responses. The results of the linear regression analysis revealed that the thermal comfort range for females was 20.39–22.19 °C, while for males it was 19.47–22.56 °C. Through discriminant analysis, participants were classified based on their thermal sensation vote (TSV), predicted mean vote (PMV), and thermal preference votes (PREF), achieving a success rate of 76.1% for females and 81.6% for males in forming the groups, which demonstrates the effectiveness of discriminant functions in predicting thermal comfort for both groups. These results highlight the importance of considering gender differences in the search for thermal comfort conditions and providing guidelines that promote the well-being of occupants and the conscious use of energy. This implies adjusting the thermal conditions according to the specific needs of males and females in classrooms, always seeking to provide a suitable environment for activities, and considering energy efficiency and users’ productivity.

Collaborative optimal scheduling of shared energy storage station and building user groups considering demand response and conditional value-at-risk

Energy storage devices can improve the utilization of clean energy and reduce the operating costs of building users. However, traditional energy storage is limited by its relatively low resource utilization and high cost. Firstly, to fully utilize the advantages of energy storage, a shared energy storage station (SESS) is introduced into the building user groups (BUGs). Second, this paper considers the incentive effect of flexible load participation in demand response (DR) on BUGs, quantifies the risky scheduling cost of wind uncertainty using conditional value-at-risk (CVaR), and then proposes a collaborative optimal scheduling model for BUGs and SESS considering DR and CVaR. Finally, the proposed model is verified by examples to achieve efficient complementation of SESS and BUGs, reduce the risk scheduling cost due to wind power uncertainty, and significantly reduce the capacity value of SESS and the electricity purchase of BUGs; meanwhile, this study carries out multi-perspective sensitivity analysis and CVaR analysis,which is informative for the refinement of the operating model of SESS and BUGs.

Pola Konsumsi Air Bersih Studi Kasus Pada MAN 1 Kota Payakumbuh

MAN 1 Payakumbuh is one of the educational institutions located in Payakumbuh. As an institution providing education with teaching and learning activities, MAN 1 Payakumbuh should be supported by adequate infrastructure and facilities, one of which is the availability of sufficient clean water. The increasing number of personnel at MAN 1 Payakumbuh from year to year, it is necessary to measure water consumption patterns. This research analyzed the number of personnel for the last five years and carry out calculations using standard usage in buildings in MAN 1 Payakumbuh . The results of this study concluded that there was an increase in water consumption patterns from year to year where in the 2018/2019 school year it was 8870 liters/day, the 2019/2020 school year was 9650 liters/day, the 2020/2021 school year was 10220 liters/day, the school year 2021/2022 is 10520 liters/day, for the 2022/2023 school year it is 10540 liters/day with an average water consumption pattern reaching 9960 liters/day. An increase in consumption patterns every year, this data can be used as a reference for the future so that in MAN 1 Payakumbuh there will be no water shortage crisis.

Data-Driven Estimation of Time-Varying Stochastic Effects on Building Heat Consumption Related to Human Interactions

Within the field of statistical modelling and data-driven characterisation of buildings’ energy performance, the focus is typically on parameter estimation of the building envelope and the energy systems. Less focus has been put on the stochastic human effect on energy consumption. We propose a new method for estimating the thermal building properties while, in parallel, estimating time-varying effects caused by the humans’ interactions with the building. We do that by combining a smooth, non-linear formulation of the energy signature method known from the literature with a hidden state formulated as a random walk to describe the human interactions with the building. The method is demonstrated on data obtained from autumn 2019 to late spring 2021 from a 900 m2 newly built school building located south of Oslo, Norway. The demonstration case has shown that the model accuracy increases and the model bias decrease when cross-validated. The estimated hidden state has also been shown to resemble the estimated combined mechanical and natural ventilation pattern controlled by the building users and operational staff. These human interactions have increased the total heat loss expressed in kilowatts per kelvin by around 50% over the course of one year from before the COVID-19 pandemic to after its outbreak.

Relationship between Self-Assessed Productivity, Gender and Age in Mixed-Mode and Fully Air-Conditioned Offices in Florianópolis, Brazil

The quality of the indoor environment and anthropometric parameters influence the users’ productivity in a building. This study aims to evaluate the influence of environmental and anthropometric characteristics on the self-assessed productivity of office building users in the humid subtropical climate of Florianópolis, Brazil. Three hybrid buildings equipped with air-conditioning and natural ventilation and one centrally air-conditioned building were considered. Indoor environmental data were obtained by means of measurements. Electronic questionnaires collected anthropometric characteristics and the perception of productivity by the users. The analysis of the users’ performance was performed using box diagrams. The results showed that women and occupants over 50 years old preferred high temperatures during the use of natural ventilation. When air-conditioning was on, the perception of productivity concerning the operative temperature did not show significant differences between anthropometric variables. It was concluded that the use of air-conditioning reduced the influence of anthropometric characteristics on self-assessed productivity and that people maintained their work performance despite the higher internal temperature variations during natural ventilation operation.

Estimating the material intensity of wooden residential houses in Finland

Improving resource efficiency in the building sector is a significant challenge, largely due to a lack of knowledge about material usage in buildings. Material intensity (MI) quantifies materials used in buildings, normalized by floor area or volume. MIs serve as indices for material stock and flow models and as an inventory approach for assessing the environmental impact of the built environment. Therefore, this study aimed to determine MIs of Finnish wooden residential houses built between 1940 and 2010 due to the dominance of them in residential building stock and their demolition rates. Factors influencing MI and cross-country comparisons were also explored because they had not been explored enough in the literature. Results showed construction method, time cohort, floor area, design choices and footprint shape impacted MI. Accounting for variability of MI was recommended, particularly when using it for material stock and flow analysis. Data and method disparities restrict cross-country comparison of MI.

A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into ‘electricity prosumers'. Additionally, managing shared energy usage and trade in smart buildings continues to be a significant difficulty. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. This study provides a coordinated smart building energy-sharing concept for smart neighborhood buildings integrated with renewable energy sources and energy storage devices within the building itself. This neighborhood energy management model's primary objective is to reduce the total power cost of all customers of smart buildings in the neighborhood by increasing the use of locally produced renewable energy. In the first stage, a group of optimum consumption schedules for each HEMS is calculated by an Improved Butterfly Optimization Algorithm (IBOA). A neighborhood energy management system (NEMS) is established in the second stage based on a consensus algorithm. A group of four smart buildings is used as a test system to evaluate the effectiveness of the suggested neighborhood smart building energy management model. These buildings have varying load profiles and levels of integration of renewable energy. In this paper, the proposed framework is evaluated by comparing it with the Grey Wolf optimization (GWO) algorithm and W/O scheduling cases. With applying GWO, the total electricity cost, peak load, PAR, and waiting time are improved with 3873.723 cents, 21.6005 (kW), 7.162225 (kW), and 87 s respectively for ToU pricing and 11217.57 (cents), 18.0425(kW), 5.984825 (kW), and 98 s respectively for CPP tariff. However, using the IBOA Improves the total electricity cost, peak load, PAR, and waiting time by 3850.61 (cents), 20.1245 (kW), 6.7922 (kW), and 53 s respectively, for ToU and 10595.8 (cents), 17.6765(kW), 5.83255(kW), and 74 s for CPP tariff. Also, it is noted that the run time is improved using GWO and IBOA by 13% and 47%, respectively, for ToU and 2% and 26% for CPP. However, the number of iterations required to obtain the optimal solution is reduced using the GWO and IBOA by 60% and 81% for ToU and 55% and 80% for CPP tariffs. The results show significant improvements obtained by applying just intelligent programming and management.

Transformers for Energy Forecast.

Forecasting energy consumption models allow for improvements in building performance and reduce energy consumption. Energy efficiency has become a pressing concern in recent years due to the increasing energy demand and concerns over climate change. This paper addresses the energy consumption forecast as a crucial ingredient in the technology to optimize building system operations and identifies energy efficiency upgrades. The work proposes a modified multi-head transformer model focused on multi-variable time series through a learnable weighting feature attention matrix to combine all input variables and forecast building energy consumption properly. The proposed multivariate transformer-based model is compared with two other recurrent neural network models, showing a robust performance while exhibiting a lower mean absolute percentage error. Overall, this paper highlights the superior performance of the modified transformer-based model for the energy consumption forecast in a multivariate step, allowing it to be incorporated in future forecasting tasks, allowing for the tracing of future energy consumption scenarios according to the current building usage, playing a significant role in creating a more sustainable and energy-efficient building usage.

Experimental and theoretical investigation of concrete filled and encased steel column under compression loading

Steel-concrete composite beams find extensive usage in high-rise buildings and bridges. The primary advantages of composite beams are high strength, high ductility, and fire resistance. In this research, the effect of the position of steel tube such as concrete encased and filled steel tube column and concrete filled steel tube column. The author bought Fe250 grade steel from the local market. The steel tube comes in two shapes: circular and rectangular. The entire column was subjected to a concentrated load until it failed. Compared to the concrete encased and filled steel tube column, the concrete filled steel tube column has a higher value of Young's modulus. The theoretical load carrying capacity of CFST is 367.74 kN, which is 19.64% lower than the experimental load carrying capacity. The theoretical load carrying capacity of CFCT is 206.80 kN, which is 28.43% lower than experimental CFCT. Theoretical load carrying capacity of CFST is 26.05% higher than the theoretical load carrying capacity of CFEST. Theoretical load carrying capacity of CFCT is 60.29% higher than theoretical load carrying capacity of CFECT. The theoretical vs experimental load difference is not less than 0.72.

IoT Based Implementation of Power Saving and Gas Leakage Detector

The implementation of IoT based power saving and gas leakage detector is an innovative approach to reduce energy consumption and enhance safety in households and commercial buildings. This system utilizes smart sensors to detect gas leaks and turn off power in rooms where there is no human presence. The sensor data is transmitted to a central server or cloud-based platform, which triggers alarms and alerts to notify residents and building managers of any potential safety hazards. The implementation of this IoT based solution can significantly reduce energy costs and improve safety by providing real-time monitoring and control of energy usage and gas leaks in buildings.

POST OCCUPANCY EVALUATION (POE) FOR ENHANCING SATISFACTION LEVEL OF COMFORTS IN MALAYSIA’S SCHOOL BUILDINGS

The physical factors of building performance in a school building must be determined to meet the users’ basic requirement of comfort level. The disruption of physical factors such as ventilation, lighting, or thermal may constitute to reduce the productivity and efficiency among users. Thus, Post Occupancy Evaluation (POE) is a significant tool to indicate the level of satisfaction and occupants’ comfort in ensuring the current issues mainly related to physical aspects such as indoor environmental quality, space utilization, energy efficiency, and so forth. Therefore, the primary purpose of this study is to assess the satisfaction level of school building users in Malaysia, including students, teachers, and staff to improve the quality of the indoor environment in a school building as the leading learning space for the future generation. The interview was conducted with 12 participants in determining the satisfaction level of building performance assessment on school buildings. Next, the survey instrument was distributed to 224 respondents in 7 different schools, to evaluate the awareness level of POE in current practice and to analyse the physical criteria to conduct the evaluation standards on school buildings in Malaysia. This study concludes that POE is the most accurate approach to estimate the quality of building performance, especially for school buildings. Also, it should be emphasized as a continuous routine of physical environment evaluation or a periodic assessment of school buildings.

New Evacuated Tube Solar Collector with Parabolic Trough Collector and Helical Coil Heat Exchanger for Usage in Domestic Water Heating

Buildings represent approximately two-thirds of the overall energy needs, mainly due to the growing energy consumption of air conditioning and water heating loads. Hence, it is necessary to minimize energy usage in buildings. Numerous research studies have been carried out on evacuated tube solar collectors, but to our knowledge, no previous study has mentioned the combination of an evacuated tube solar collector with a parabolic trough collector and a helical coil heat exchanger. The objective of this paper is to evaluate the thermal behavior of an innovative evacuated tube solar collector (ETSC) incorporated with a helical coil heat exchanger and equipped with a parabolic trough collector (PTC) used as a domestic water heater. To design the parabolic solar collector, the Parabola Calculator 2.0 software was used, and the Soltrace software was used to determine the optical behavior of a PTC. Moreover, an analytical model was created in order to enhance the performance of the new model of an ETSC by studying the impact of geometric design and functional parameters on the collector’s effectiveness. An assessment of the thermal behavior of the new ETSC was performed. Thus, the proposed analytical model gives the possibility of optimizing ETSCs used as domestic water heaters with lower computational costs. Furthermore, the optimum operational and geometrical parameters of the new ETSC base-helical tube heat exchanger include a higher thermal efficiency of 72%. This finding highlights the potential of the heat exchanger as an excellent component that can be incorporated into ETSCs.

Impacts of COVID-19 on educational buildings energy consumption: case study of the university of Jordan

The global lockdowns adopted by many countries to combat the outbreak of the COVID-19 pandemic led to a significant transformation in the teaching methods adopted in higher education institutions toward dependence on online learning systems. Although this pandemic has placed a technical and financial burden on academic institutions to facilitate the successful transition to online learning, it provides opportunities to understand the impacts of adopting new policies and strategies to improve the efficient utilization of resources and thus reduce operational costs. The detailed analyses of the changes in energy consumption can support assessing the potential savings in electricity bills with the wide-scale adoption of online learning methods in the future as a business as usual to improve and modernize the education systems. This paper provides a detailed analysis of the electrical energy consumption of the buildings within the campus of the University of Jordan. The diversity of building types on the campus (e.g., university hospital, humanities schools, scientific schools) supports the provision of more general and robust recommendations to extend the results to other institutions, particularly in developing countries. The Energy Use Intensity (EUI) per unit area and EUI per student are employed for the first time for benchmarking the energy usage in educational buildings in Jordan. Overall, the analyses show that the total electricity consumption in 2020 was significantly lower than in 2019, with a decrease of 20.8% from 27.7 GWh in 2019 to 21.9 GWh in 2020. It is also found that the most significant reduction occurred in the humanities buildings (i.e., a 39% drop in energy consumption). However, this volume of energy reduction is still relatively low, considering the absence of students. Furthermore, the hospital has an extremely high EUI value (161 kWh/m2/year) compared to the other categories (e.g., the EUI for humanities schools is 32.5 kWh//m2/year). To conclude, the electrical energy consumption data suggests that there may be significant opportunities for energy conservation in all building categories, especially in the hospital.

General Knowledge Representation and Sharing, with Illustrations in Risk/Emergency Management

Many decision-making tasks, including the sustainability-oriented ones and those related to the management of risks or emergencies, must gather, integrate, and analyze an important amount of information of various kinds and origins. Hence, how should information be best organized and shared by agents – people or software – for all and only the pieces of information looked for by these agents to be maximize their retrieval, reuse, organization and analysis by these agents? To that end, various logic-based knowledge representation (KR) and sharing (KS) techniques, and hence KR bases, have been used. However, most KS researchers focus on what this article defines as “restricted KR and KS”, where information providers and consumers can or have to discuss for solving information ambiguities and other problems. The first part of this article highlights the usefulness of “general KR and KS” and, for supporting them, provides a panorama of complementary techniques, and hence, indirectly, best practices or kinds of tools to use for general KS purposes. These techniques collectively answer research questions about how to support Web users in the collaborative building of KR bases. The second part uses the risk/emergency management domain to illustrate the ways different types of information can be represented to support general KS.

Embodied carbon savings of co-living and implications for metrics

In light of the climate crisis and conflicting political ambitions in many countries to rapidly increase the number of dwellings, what housing strategies could reduce emissions? Co-living is one strategy sometimes highlighted but rarely implemented in mainstream construction practices. Using two Swedish case studies, the potential embodied carbon savings are explored for co-living designs. When comparing building designs, normalisation of impacts or energy use per floor area is unequivocally the norm. The present comparison between co-living and traditional apartment design indicates an embodied carbon savings at the building level of 10–20% depending on whether embodied carbon is normalised per gross or residential floor area. However, normalisation per capita (inhabitant) shows substantially higher savings of 21–36% depending on the case studied. The effect of different metrics is illustrated to quantify potential embodied carbon savings of non-mainstream building design solutions such as co-living. Even more substantial embodied carbon savings can be achieved by avoiding new construction through the ability of enabling a more efficient use of indoor space. The need for rethinking carbon and space metrics will help the building sector meet emission targets. Practice relevance Evidence is provided to show that design for co-living could be one way to offer a climate-efficient and qualitative housing alternative for single households in many countries. However, to visualise such potentials, developers are recommended to use additional metrics when evaluating how resource or climate-efficient are alternative designs. Traditional metrics such as kW h or kg CO2e/m2 of gross or heated floor area ought to be complemented by displaying resource use or embodied carbon per designed number of building user and per accessible floor area for each user. Up-to-date generic values are provided for the embodied carbon of different types of space. These can be used in early planning to display the consequences of the number of kitchens and bathrooms and their space occupation in client decisions and early architectural design.

A Game-Theoretic Approach to Energy-Efficient Elevator Scheduling in Smart Buildings

Buildings, producing more carbon footprints than the transportation sector, account for a significant portion of the United States’ total energy consumption. By designing modern automation techniques, smart buildings can significantly reduce energy consumption, protect the environment, and consequently improve quality of life. This article focuses on the automation of elevator scheduling, which is an NP-Hard problem, to reduce energy usage in smart buildings and improve users’ quality of experience. We propose an optimal mathematical model for the elevator scheduling problem using integer programming. We then propose a novel game-theoretic approach that captures interactions within the elevator system to reduce energy consumption and enhance user experience. We propose a request coalition formation game, where nonoverlapping coalitions of user requests are served by elevators to minimize their movements and energy consumption while reducing service time and stops for users. We analyze the performance of our proposed approach using the optimal solution as a benchmark and Nearest Car and Fixed Sectoring algorithms as rivals. The experiments show that our approach is significantly efficient in terms of energy consumption and service time, making it suitable for smart buildings.