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1 – 10 of over 64000Elijah Kusi, Isaac Boateng and Humphrey Danso
Using building information modelling (BIM) technology, a conventional structure in this study was converted into a green building to measure its energy usage and CO2 emissions.
Abstract
Purpose
Using building information modelling (BIM) technology, a conventional structure in this study was converted into a green building to measure its energy usage and CO2 emissions.
Design/methodology/approach
Digital images of the existing building conditions were captured using unmanned aerial vehicle (UAV), and were fed into Meshroom to generate the building’s geometry for 3D parametric model development. The model for the existing conventional building was created and converted to an energy model and exported to gbXML in Autodesk Revit for a whole building analysis which was carried out in the Green Building Studio (GBS). In the GBS, the conventional building was retrofitted into a green building to explore their energy consumption and CO2 emission.
Findings
By comparing the green building model to the conventional building model, the research found that the green building model saved 25% more energy while emitting 46.8% less CO2.
Practical implications
The study concluded that green building reduces energy consumption, thereby reducing the emission of CO2 into the environment. It is recommended that buildings should be simulated at the design stage to know their energy consumption and carbon emission performance before construction.
Social implications
Occupant satisfaction, operation cost and environmental safety are essential for sustainable or green buildings. Green buildings increase the standard of living and enhance indoor air quality.
Originality/value
This investigation aided in a pool of information on how to use BIM methodology to retrofit existing conventional buildings into green buildings, showing how green buildings save the environment as compared to conventional buildings.
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Omprakash Ramalingam Rethnam and Albert Thomas
The building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes…
Abstract
Purpose
The building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes essential in this scenario to realize the global net-zero goals. The purpose of the proposed study is to evaluate the impact of the widespread adoption of such guidelines in a building community in the context of mixed-mode buildings.
Design/methodology/approach
This study decentralizes the theme of improving the energy efficiency of the national building stock in parcels by proposing a community-based hybrid bottom-up modelling approach using urban building energy modelling (UBEM) techniques to analyze the effectiveness of the community-wide implementation of energy conservation guidelines.
Findings
In this study, the UBEM is developed and validated for the 14-building residential community in Mumbai, India, adopting the framework. Employing Energy Conservation Building Code (ECBC) compliance on the UBEM shows an energy use reduction potential of up to 15%. The results also reveal that ECBC compliance is more advantageous considering the effects of climate change.
Originality/value
In developing countries where the availability of existing building stock information is minimal, the proposed study formulates a holistic framework for developing a detailed UBEM for the residential building stock from scratch. A unique method of assessing the actual cooling load of the developed UBEM is presented. A thorough sensitivity analysis approach to investigate the effect of cooling space fraction on the energy consumption of the building stock is presented, which would assist in choosing the appropriate retrofit strategies. The proposed study's outcomes can significantly transform the formulation and validation of appropriate energy policies.
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Shiyu Wan, Yisheng Liu, Grace Ding, Goran Runeson and Michael Er
This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose…
Abstract
Purpose
This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.
Design/methodology/approach
The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.
Findings
This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.
Originality/value
Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.
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Baabak Ashuri, Jun Wang, Mohsen Shahandashti and Minsoo Baek
Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current…
Abstract
Purpose
Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking.
Design/methodology/approach
Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency.
Findings
The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers.
Originality/value
The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.
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Ana Carolina Franco De Oliveira, Cristiano Saad Travassos do Carmo, Alexandre Santana Cruz and Renata Gonçalves Faisca
In developing countries, such as Brazil, the construction sector is consistently focused on the construction of new buildings, and there is no dissemination of the preservation…
Abstract
Purpose
In developing countries, such as Brazil, the construction sector is consistently focused on the construction of new buildings, and there is no dissemination of the preservation, restoration and maintenance of historic buildings. Idle buildings, due to the use and lack of maintenance, present pathological manifestations, such as moisture problems that compromise specially their thermal and energy performance. With this in mind, the purpose of this work is to create a digital model using terrestrial photogrammetry and suggest retrofit interventions based on computer simulation to improve the thermal and energy performance of a historical building.
Design/methodology/approach
The proposed methodology combined terrestrial photogrammetry using common smartphones and commercial software for historical buildings with building information modeling (historic building information modeling (HBIM)) and building energy modeling (BEM). The approach follows five steps: planning, site visit, data processing, data modeling and results. Also, as a case study, the School of Architecture and Urbanism of the Fluminense Federal University, built in 1888, was chosen to validate the approach.
Findings
A digital map of pathological manifestations in the HBIM model was developed, and interventions considering the application of expanded polystyrene in the envelope to reduce energy consumption were outlined. From the synergy between HBIM and BEM, it was concluded that the information modeled using photogrammetry was fundamental to create the energy model, and simulations were needed to optimize the possible solutions in terms of energy consumption.
Originality/value
Firstly, the work proposes a reasonable methodology to be applied in development countries without sophisticated technologies, but with acceptable precision for the study purpose. Secondly, the presented study shows that the use of HBIM for energy modeling proved to be useful to simulate possible solutions that optimize the thermal and energy performance.
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Amneh Hamida, Abdulsalam Alsudairi, Khalid Alshaibani and Othman Alshamrani
Buildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy…
Abstract
Purpose
Buildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.
Design/methodology/approach
An Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.
Findings
The MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.
Research limitations/implications
This study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.
Originality/value
By providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.
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Vivian W.Y. Tam, Lei Liu and Khoa N. Le
This paper proposes an intact framework for building life cycle energy estimation (LCEE), which includes three major energy sources: embodied, operational and mobile.
Abstract
Purpose
This paper proposes an intact framework for building life cycle energy estimation (LCEE), which includes three major energy sources: embodied, operational and mobile.
Design/methodology/approach
A systematic review is conducted to summarize the selected 109 studies published during 2012–2021 related to quantifying building energy consumption and its major estimation methodologies, tools and key influence parameters of three energy sources.
Findings
Results show that the method limitations and the variety of potential parameters lead to significant energy estimation errors. An in-depth qualitative discussion is conducted to identify research knowledge gaps and future directions.
Originality/value
With societies and economies developing rapidly across the world, a large amount of energy is consumed at an alarming rate. Unfortunately, its huge environmental impacts have forced many countries to take energy issues as urgent social problems to be solved. Even though the construction industry, as the one of most important carbon contributors, has been constantly and academically active, researchers still have not arrived at a clear consensus for system boundaries of life cycle energy. Besides, there is a significant difference between the actual and estimated values in countless current and advanced energy estimation approaches in the literature.
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Greg Foliente and Seongwon Seo
A systematic approach is needed to engage a broad range of stakeholders to reduce greenhouse gas (GHG) emissions from energy use in the building sector. The purpose of this paper…
Abstract
Purpose
A systematic approach is needed to engage a broad range of stakeholders to reduce greenhouse gas (GHG) emissions from energy use in the building sector. The purpose of this paper is to develop a systems‐based bottom‐up approach for this purpose, and to demonstrate its application in a case study of office building stock in the State of New South Wales (NSW) in Australia.
Design/methodology/approach
A conceptual framework for the general method is developed based on a cross‐typology matrix of energy consumption and supply on the one hand, and intervention schemes or policy instruments on the other. This is then tested and demonstrated using a case study of commercial office building stock, with building energy demand calculated using a validated computer energy simulation tool for a representative set of office buildings within a local government area (LGA). The energy consumption and associated GHG emissions are then aggregated up from LGA to the whole State. The impact projections of different intervention schemes are compared and mapped spatially across the State.
Findings
Results have demonstrated the significance and capability of the proposed approach, in allowing quantitative comparisons of the relative effectiveness of a specific set of regulatory, technical and behavioral scenario settings on the spatial distribution and trends in energy consumption and GHG emissions of the NSW office building stock to 2020.
Research limitations/implications
Further case studies involving mixed building types and specific building types with greater granularity of modelling details, energy use and supply options, and spatial resolution are needed.
Originality/value
The structured cross‐typology approach is a novel contribution to bottom‐up modelling approaches to designing and/or assessing the effectiveness of specific policy instruments, alone or in combination. It will enable policy makers, property portfolio owners, utility companies and building tenants to assess the broader impacts of their specific actions towards a common goal.
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Nehal Elshaboury, Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf and Ashutosh Bagchi
The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy…
Abstract
Purpose
The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.
Design/methodology/approach
This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R2], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.
Findings
The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R2, RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.
Originality/value
This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.
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Energy saving of buildings is a big issue because building energy occupies about 25% of national energy. To reduce building energy, many countries try to control building energy…
Abstract
Energy saving of buildings is a big issue because building energy occupies about 25% of national energy. To reduce building energy, many countries try to control building energy by laws, such as building energy regulations. This study has two objectives. The first is to evaluate building energy efficiency rate of a specific building using annual energy analysis software based on BIM tools. The second objective is to reduce building energy usage adopting passive and active architectural strategies under the Korean building energy rating regulation. A newly constructed public library building was selected as energy simulation base model, and a few active and passive energy saving strategies were applied to the same building. Energy plus was adopted as a simulation tool. The calculated annual source energy usage per area for alt model was 210.7 kWh/m2 yr, and the value for base model was 355 kWh/m2 yr. The building energy efficiency rate for alt model is the first class under Korean building energy rating regulation.
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