Using Regression Model to Develop Green Building Energy Simulation by BIM Tools

Tahmasebinia, Faham; Jiang, Ruifeng; Sepasgozar, Samad M. E.; Wei, Jinlin; Ding, Yilin; Ma, Hongyi

doi:10.3390/su14106262

Cited by 16 publications

(11 citation statements)

References 50 publications

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“…One form of providing education can be through simulating disaster events with sufficient frequency so that both staff and visitors can practice and get used to acting when a disaster occurs later. In addition, a study (Tahmasebinia et al, 2022) stated that in existing buildings, reconstruction based on simulation results can increase the guarantee of the safety of human life and property.…”

Section: Discussionmentioning

confidence: 99%

Preparedness of Rumah Ilmu Library in Facing Fire Disasters: Safety Education for Campus Society

Dewi,

Yasmin,

Hanan

et al. 2023

jse

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Due to its contents and amenities, Rumah Ilmu, a library building at Universitas Negeri Semarang, is vulnerable to fire disasters. The goal of this study is to analyse the safety aspects of Rumah Ilmu building. In this study, a qualitative methodology and a descriptive strategy were applied and a direct interview with the informants was conducted to collect the data needed. This study was carried out between February and March of 2023. Rumah Ilmu building has met all the government's standards with the fulfilment of 10 components (71.42%), However, 3 components (21.42%) consisting of fire extinguishers, evacuation directions, and assembly points have been implemented but have not met the standards, and 1 component (7.16%) has not been implemented at all, namely the main gate. Suggestions for the preparedness of the Rumah Ilmu in dealing with disasters include increasing the number of fire extinguishers, sprinklers, and rescue facilities, such as installing emergency lighting, adding an area for assembly points, checking periodically related to exit door locking, and arranging an evacuation plan. Additionally, disaster mitigation education should be provided through training, educational videos, and posters to staff and visitors.

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Section: Discussionmentioning

confidence: 99%

Preparedness of Rumah Ilmu Library in Facing Fire Disasters: Safety Education for Campus Society

Dewi,

Yasmin,

Hanan

et al. 2023

jse

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show abstract

“…This case study focuses on the Abercrombie building at the University of Sydney, utilizing Building Information Modeling (BIM) data encapsulated in gbXML format to perform regression analysis. The objective is to ascertain the correlation between diverse building design elements and Energy Use Intensity (EUI) [42]. In this investigation, Autodesk Green Building Studio (GBS) was leveraged to conduct energy simulations for various prototype models, including L-shaped, U-shaped, H-shaped, diamond-shaped, and rectangular configurations.…”

Section: Abercrombie Business School (Abs)mentioning

confidence: 99%

“…This investigation was built upon two studies mentioned in the literature review, both of which utilized linear regressions to conduct a linear analysis between parameters and EUI [42,43]. However, linear regression may be an overly optimistic analysis method.…”

Section: Regression Analysismentioning

confidence: 99%

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Tahmasebinia,

Lin,

et al. 2024

Buildings

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The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs.

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“…For this purpose, the k-mean algorithm is used to detect and analyze the size of a throwing point in the data. Tahmasebinia et al [19]. is study aimed to identify the critical design parameters for building energy performance to assist architects in the early stages of building design and investigate their relationship.…”

Section: Literature Reviewmentioning

confidence: 99%

Providing an Expert System to Manage Energy Consumption in Small‐ and Medium‐Sized Industries Using Soft Computing

2022

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The increasing growth of electricity consumption, the need for its continuous supply, and the impossibility of storing this vital energy are among the primary and essential reasons for preventing any interruption in supply and proper planning regarding energy consumption management, especially in industry. The innovation of this research is the design of a decision-making system to help decision-making in energy management in small- and medium-sized industries using the knowledge of experts in small- and medium-sized industries and the electricity industry in the field of energy consumption management and extracting relevant expertise rules to create a knowledge base. Therefore, the main advantage of the research is to provide an expert system for the correct management of energy consumption in small and medium industries using soft computing. Another advantage of the research is the use of uncertain decision-making variables in this field; instead of using words and linguistic restrictions in this research, modeling them in the system using the principles of fuzzy logic is used. The main objectives of this research include determining the effective variables in determining the amount of energy production for small and medium industries, explaining the existing tacit knowledge and turning it into explicit knowledge, and providing an expert system for managing energy consumption in this field. Based on the results of field studies and the research literature, effective variables in determining the amount of electricity generation were identified. By extracting the knowledge of industry experts, the tacit knowledge available as explicit knowledge in the storage knowledge base and the system was designed to manage electrical charge consumption. One of the achievements of this system is its creation and measurement using various data and results obtained from small and medium industries. Finally, the results obtained from the expert system in comparing the output of expert opinions and the system’s performance in managing the amount of energy production showed that the usefulness of the expert system was designed. The system uses the knowledge base to make decisions such as an expert human. According to the main results of the research based on comparing the performance of the system with the Bayesian inference networks, it showed that there is an acceptable correlation between the data and the prediction system. Also, to evaluate the acquisition of data using a statistical test, it has been determined that the total number of blackout hours in different areas of Tehran in the base year was more than the current year.

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Using Regression Model to Develop Green Building Energy Simulation by BIM Tools

Cited by 16 publications

References 50 publications

Preparedness of Rumah Ilmu Library in Facing Fire Disasters: Safety Education for Campus Society

Preparedness of Rumah Ilmu Library in Facing Fire Disasters: Safety Education for Campus Society

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Providing an Expert System to Manage Energy Consumption in Small‐ and Medium‐Sized Industries Using Soft Computing

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