Search results

This study evaluates the aesthetic perception of photovoltaic (PV) systems situated at various locations on an apartment building facade, comparing them with the original facade. It also aims to understand how aesthetic dimensions influence the perception of PV installations in diverse building locations. Moreover, it aims to create a framework that will guide for installing PV installations considering both their functionality and aesthetics.

The study uses a mixed-method approach, including qualitative and quantitative approaches. It includes a literature review and a questionnaire. 418 participants evaluated different PV-embedded facades using a Likert scale across various aesthetic variables.

The findings indicate that aesthetic perceptions of PV vary by the location of installation. It also shows that all aesthetic dimensions affect PV installation aesthetics, with location-specific preferences. For original elevation, compatibility and simplicity are given precedence over blending and coherence for windows, creativity and harmony for facades, functionality and harmony for balconies, and innovation potential and simplicity for roofs.

This study focuses on a single building type; further investigation is required to examine other building types. It also examined one PV technology with common visual properties, but future studies can examine others. Additional research is needed to compare the participating groups and the effect of their sociodemographic factors, using on-site surveys and interviews.

Few studies have investigated how PV systems affect apartment building users' architectural aesthetic perception. The results of this study make a valuable contribution to the field of sustainable architecture by providing practical guidance for architects, engineers, stakeholders, and researchers who are interested in integrating aesthetic, user-centric considerations into renewable energy solutions.

Contemporary construction techniques provide benefits of speed and cost savings on a large scale, and is viable in urban regions with exorbitant housing demand. In rural areas, where scale and access to technology are unavailable, locally prevalent vernacular architecture and methods are more suitable. Although vernacular construction techniques have historically proven more sustainable and climate-sensitive, the lack of skilled labour and lack of versatility in material selection limits its application on large-scale projects. This study explores the choice of building design and technology, from the context of embodied energy, carbon and other life cycle impacts for housing construction.

Life cycle assessment (LCA) that evaluates impacts due to the products/processes is used to analyse different construction techniques. Further a detailed estimation of embodied carbon and embodied energy is done for both “vernacular” and “contemporary” choices of construction methodology for a case study project.

The building constructed using vernacular techniques has lower embodied carbon and energy by over 30% compared to the other clusters designed using contemporary confined masonry techniques. However, with a few external interventions the contemporary methods can be implemented with improved sustainability.

The limitation of the study is that it presents a case study-based exploration into comparing construction techniques to provide a practical understanding of making sustainable design choices and, hence, is limited to two construction methods. However, the same method could be extended to compare other construction techniques. Furthermore, it does not present a whole building LCA since the operating phase impacts are assumed to be fairly constant for such housing type, irrespective of the chosen method. Similarly, the demolition phase or the potential of reuse of the waste generated, water consumption and cultural and social heritage are not investigated in comparing the alternatives. Nevertheless, future studies could perform extensive exploratory and modelling studies on the operation phase and demolition phase to understand these impacts further.

In mass housing projects that belong to the so-called “affordable housing” or low-income housing category, sustainability concerns are not yet at the forefront of the decision-making process. Therefore, this study emphasizes the importance of incorporating sustainability into building design and construction and making sustainability accessible to even low-income communities. Adequate planning, social awareness initiatives and imparting skills and knowledge of sustainability to these communities are of utmost importance. The choice of design and materials should be encouraged by keeping in mind lower upfront costs as well as low maintenance and operational costs.

The primary implications of the study are that the vernacular technologies are much superior in terms of sustainability in comparison to conventional construction of RCC framed structures as well as contemporary construction methods such as confined masonry. However, the implementation of such techniques presents significant challenges such as a lack of skilled forces, increased maintenance and lack of flexibility to minor modifications. Hence, although being a sustainable choice its acceptance and execution present practical difficulties. Therefore, this study primarily aims to reinforce the belief in vernacular architecture and techniques to build sustainable and resilient communities while highlighting the challenges of the modern world in implementing them.

Most studies advocate using construction methods based on their ease of implementation, maintenance or cost. However, this study highlights the importance of considering the aspect of sustainability in the context of the choice of methods for housing construction in urban and semi-urban areas. This study also addresses the need not to overlook vernacular construction technologies while selecting technology for housing for low-income communities.

The residential buildings sector has a high priority in the climate change adaptation process due to significant CO₂ emissions, high energy consumption and negative environmental impacts. The article investigates how, conversely speaking, the residential buildings will be affected by climate change, and how to improve existing structures and support long-term decisions.

The climate dataset was created using the scenarios determined by the Intergovernmental Panel on Climate Change (IPCC), and this was used in the study. Different building envelope and Heating, Ventilating and Air Conditioning (HVAC) systems scenarios have been developed and simulated. Then, the best scenario was determined with comparative results, and recommendations were developed.

The findings reveal that future temperature-increase will significantly impact buildings' cooling and heating energy use. As the outdoor air temperatures increase due to climate change, the heating loads of the buildings decrease, and the cooling loads increase significantly. While the heating energy consumption of the house was calculated at 170.85 kWh/m² in 2020, this value shall decrease significantly to 115.01 kWh/m² in 2080. On the other hand, the cooling energy doubled between 2020 and 2080 and reached 106.95 kWh/m² from 53.14 kWh/m² measured in 2020.

Single-family houses constitute a significant proportion of the building stock. An in-depth analysis of such a building type is necessary to cope with the devastating consequences of climate change. The study developed and scrutinised energy performance improvement scenarios to define the climate change adaptation process' impact and proper procedure. The study is trying to create a strategy to increase the climate resistance capabilities of buildings and fill the gaps in this regard.

This proposal aims to forecast energy consumption in residential buildings based on the effect of opening and closing windows by the deep architecture approach. In this task, the developed model has three stages: (1) collection of data, (2) feature extraction and (3) prediction. Initially, the data for the closing and opening frequency of the window are taken from the manually collected datasets. After that, the weighted feature extraction is performed in the collected data. The attained weighted feature is fed to predict energy consumption. The prediction uses the efficient hybrid multi-scale convolution networks (EHMSCN), where two deep structured architectures like a deep temporal context network and one-dimensional deep convolutional neural network. Here, the parameter optimization takes place with the hybrid algorithm named jumping rate-based grasshopper lemur optimization (JR-GLO). The core aim of this energy consumption model is to predict the consumption of energy accurately based on the effect of opening and closing windows. Therefore, the offered energy consumption prediction approach is analyzed over various measures and attains an accurate performance rate than the conventional techniques.

An EHMSCN-aided energy consumption prediction model is developed to forecast the amount of energy usage during the opening and closing of windows accurately. The emission of CO₂ in indoor spaces is highly reduced.

The MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN.

The findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.

The Sustainable Development Goals (SDGs) were established by the UN in 2015 as an international call to action to end poverty, protect the environment, and ensure that everyone will live in peace and prosperity by the year 2030. (UNDP, 2015). Out of the 17 Goals, Goal 4 talks about “ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all.” The SDGs are essential to architectural education since buildings account for 40% of extracted materials used in construction and 39% of global energy-related carbon emissions. (Hendawy, 2023) To accomplish these objectives, the architecture curriculum must be up-to-date and efficient. It specifically takes into account the field of architecture. The purpose of this paper is to propose a framework for curriculum guidelines of B.Arch. design studios, focusing on the parameter designing for inclusivity (UIA2023CPH, 2020) of SDGs.

Models, approaches and theories of curriculum development are studied. An evaluation matrix is made to evaluate different curriculum framework for government colleges on their incorporation of SDGs. A survey/questionnaire and interviews with academicians from architecture are conducted to analyze the incorporation of SDGs in design studios and find recommended solutions for its incorporation.

Finally, this research paper proposes a set of Architecture curriculum guidelines based on the evaluation framework for B.Arch. and an exemplary curriculum design model for the Architecture design studio which is evaluated through the desk critique stage for analyzing its functionality and applicability.

Despite the emphasis on SDGs in the present scenario, the “Council Of Architecture (Minimum Standards of Architecture Education in India) Regulations, 2020” does not include or mention SDGs as a key aspect (Architecture, 2020).

In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design.

A methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth.

The results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds.

The proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.

Fresh out of the two-century-old British legacy, Bangladesh, formerly known as East Pakistan from 1947 to 1971, was searching for a post-colonial architectural style. Colonial architecture in the region in general often imposed imported European elements, ignoring the preceding legacies of the Sultanate and the Mughals. The critical challenge was to find a balance between the prevailing high modernism in architecture and the local vernacular and climatic forces. The Pakistani government invited international architects to fill the gap left by a non-existent local architectural industry. Unfortunately, their work has rarely been properly analyzed. With selected case studies, this paper analyzes their work in an attempt to explore their contribution to creating a national architectural identity.

This study uses a case study approach with selected architectural projects from the period. It uses research tools such as systematic analysis of drawings, volumes and photographs and archival research.

The international architects took inspiration from the strong vernacular and climatic forces of the region. The resultant expressions of the two-decade-long search in their combined body of work are some of the finest examples of vernacular and climate-responsive architecture in the region. They transcended the regular international style and became context-specific and unique. The quest for East Pakistan's post-colonial architectural identity was partially met by the newly found identity through vernacular and climate-responsive adaptation in architecture.

This study explores how a unified vernacular and climate-responsive adaptations potentially shaped the post-colonial architectural identity of the region. No prior study exists on this issue for the time period.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

This study aims to explore how to structure an energy organization to be more agile in the context of digitalization and find the common success factors and challenges the organizations face to transform.

A qualitative multiple-case study of an energy company and an online market company, both from the Nordic countries, was conducted in 2023. Data were collected through in-depth individual interviews with 16 participants.

This research provides valuable insights into the challenges and critical success factors crucial for a successful digital transformation. The study illuminates the interplay between technological advancements and organizational shifts, the adoption of agile methodologies, the importance of inclusive leadership and the integration of autonomous teams in realizing digital transformation goals. The research emphasizes the profound impact of these factors on the transformational journey within organizations. In particular, the adoption of agile methodologies takes on heightened significance in the swiftly evolving business landscape of today, calling for a transition from project-centric approaches to more adaptive and sustainable product-centric models.

The study contributes to the practice and enhanced understanding of organizing business and technology teams in energy companies to achieve fast flow. The analysis and discussion of various empirical findings shed light on the success factors and challenges of digital transformation – issues that many organizations are currently or will likely soon be grappling with. The impact of different organizational structures on agility in an organization has yet to be thoroughly studied.

This study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.

A standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO₂ levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.

Discrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO₂ levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.

This paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.