Energy Analysis, Simulation, and Comparison of a Commercial Building Using Standard Approach and AECOsim

Challenges for energy and carbon modeling of high-rise buildings: The case of public housing in Hong Kong

Traditional energy modeling methods are usually time-consuming and labour-intensive, so energy simulation is rarely performed early in building design. If a Building Energy Model (BEM) can be seamlessly generated from a Building Information Modeling (BIM) model, the energy simulation process can be much more efficient and better integrated in design. The concerns about BIM to BEM data transfer integrity and the reliability of simulation results are preventing wider adoption of BIM-based energy simulation. This study aimed to address these two obstacles and increase energy modelers’ confidence in using BIM for energy analysis. Green Building Studio (GBS) was used to simulate energy use and generate eQuest and EnergyPlus input files. Two building types were modeled in Revit with various iterations and BEM input files downloaded from GBS were compared line by line to identify and classify discrepancies. Simulation results from BIM-based and traditional modeling were compared to test reliability and showed unexpectedly good agreement across methods.

Traditional energy modeling methods are usually time-consuming and labour-intensive, so energy simulation is rarely performed early in building design. If a Building Energy Model (BEM) can be seamlessly generated from a Building Information Modeling (BIM) model, the energy simulation process can be much more efficient and better integrated in design. The concerns about BIM to BEM data transfer integrity and the reliability of simulation results are preventing wider adoption of BIM-based energy simulation. This study aimed to address these two obstacles and increase energy modelers’ confidence in using BIM for energy analysis. Green Building Studio (GBS) was used to simulate energy use and generate eQuest and EnergyPlus input files. Two building types were modeled in Revit with various iterations and BEM input files downloaded from GBS were compared line by line to identify and classify discrepancies. Simulation results from BIM-based and traditional modeling were compared to test reliability and showed unexpectedly good agreement across methods.

In today's world a massive amount of energy is used by the building industry. The non-renewable resources are increasingly being exhausted because of the intense use. Construction industry accounts for large quantities of greenhouse gas emissions which lead to climate change and ozone layer depletion. This adverse environmental impact stimulates the need for innovative construction strategies that reduce the overall carbon emissions and embodied energy that are represented. The study focused on a conventional building whose slab and block work is changed with different slab and block work and the embodied energy calculation of energy analysis, and the sustainability index and its different parameters that was compared with all the Green criteria recommended by SVAGRIHA a denotation for ‘Small Versatile Affordable Green Rating for Integrated Habitat Assessment’ council. It was found that the total construction of the Green building is more recommended in terms of environmental and ecological parameters. Semi Pro and Revit are used for them to calculate embodied energy and effective design respectively. A radar diagram has also been drawn to help with selecting the type of roofing and block work with respect to sustainability indicators.

Building energy simulation has become an important method for reducing energy use and carbon dioxide emissions in sustainable building design. In the last decade, we have witnessed the employment of building information model (BIM) and internet technologies to be harnessed for energy-efficient building design. In this research the data exchange between a Building Information Model (BIM) and a Building Energy Model (BEM) is investigated. In previous research energy simulation engines are integrated into the BIM application for BIM users to evaluate the design directly at the conceptual design stage. After the conceptual design stage the energy analysis task is shifted to professional engineers. In a conventional process, engineers analyse the BEM, which has been previously exported manually from the BIM, and optimize the parameters in the BEM. These results are then used to manually update the BIM used in design. However, this process is cumbersome and error-prone as it is hard to keep the BEM up-to-date with the BIM, and the optimization of the parameters on the BEM cannot be easily synchronized back to the BIM model. The increasing uses of the internet for data exchange and new database technologies have the potential to change this. We employ service-oriented architecture (SOA) to connect the components of services in each side of BIM and BEM. Based on a critical study of data and schema in the BIM and the BEM model, we propose a SOA based BIM and BEM exchange framework that can be used to support the collaboration and information synchronization among the participants in a sustainable design project. This framework is exemplified using a case study.

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields.

Few topics are more relevant in current times than energy management. Fast depleting reserves and record-high prices of fossil fuels and global climatic change are forcing a strategic rethink towards the way we deal with our energy needs, across the globe. Better energy management and energy consumption reduction could help Qatar's economy better achieve its sustainability targets. With buildings consuming over 40% of national energy consumption, reducing in-building energy consumption represent a huge opportunity to achieve energy and corresponding Green House Gas (GHG) emissions reduction. Buildings consume massive amount of Energy, resulting from heavy electrical loads from lighting, cooling and appliance usage. Thus, reducing the consumption of energy in residential and commercial buildings will have a huge impact on total energy savings. The majority of buildings which will be standing in 2050 have already been built, so building owners need to retrofit their buildings in such a way as to optimize greenhouse gases emissions and energy consumption reduction. This research presents a framework to yield optimal energy reduction, to help decided spending of energy retrofit budget in most cost-effective and result oriented manner, by identifying existing building stock with a potential of maximum energy reduction. Existing approaches for building energy performance analysis are either prohibitively expensive (e.g. detailed energy audits by certified experts) or inadequately granular (not providing enough energy feedback; e.g. carbon calculators, energy benchmarks, ROI curves). Also, existing energy modelling processes require weeks or months to construct, before useful information can be provided to guide retrofit decisions. Thus, there is need to complement existing approaches with innovative approaches to building energy modelling. The presented research aims to address technical and cost challenges associated with energy consumption feedback and retrofit decision making. Research aim is to develop a technology driven framework to provide a quick and cost-effective method of undertaking building energy audits using Building Information Modelling (BIM) and Energy Simulation technologies. Implementation of such an framework will provide a relatively accurate and inexpensive decision support tool to provide useful energy consumption related information to building users and decision makers. Presented research builds on previous pilot conducted by authors, which demonstrated that BIM/IFC based approaches provide a feasible alternative to conduct energy analysis of existing buildings, provided various correlations are built into the model. The approach does not require specialist energy assessor, auditor or a software expert. After initial calibration, results were obtained within a 5% margin of accuracy. The results could be used for preliminary energy analysis, for exploring different what-if scenarios, providing interactive feedback to building users and for exploring various alternatives to enhance building performance using renewable energy.

Abstract. We perform a formal attribution study of upper- and lower-stratospheric ozone changes using observations together with simulations from the Whole Atmosphere Community Climate Model. Historical model simulations were used to estimate the zonal-mean response patterns (“fingerprints”) to combined forcing by ozone-depleting substances (ODSs) and well-mixed greenhouse gases (GHGs), as well as to the individual forcing by each factor. Trends in the similarity between the searched-for fingerprints and homogenized observations of stratospheric ozone were compared to trends in pattern similarity between the fingerprints and the internally and naturally generated variability inferred from long control runs. This yields estimated signal-to-noise (S∕N) ratios for each of the three fingerprints (ODS, GHG, and ODS + GHG). In both the upper stratosphere (defined in this paper as 1 to 10 hPa) and lower stratosphere (40 to 100 hPa), the spatial fingerprints of the ODS + GHG and ODS-only patterns were consistently detectable not only during the era of maximum ozone depletion but also throughout the observational record (1984–2016). We also develop a fingerprint attribution method to account for forcings whose time evolutions are markedly nonlinear over the observational record. When the nonlinearity of the time evolution of the ODS and ODS + GHG signals is accounted for, we find that the S∕N ratios obtained with the stratospheric ODS and ODS + GHG fingerprints are enhanced relative to standard linear trend analysis. Use of the nonlinear signal detection method also reduces the detection time – the estimate of the date at which ODS and GHG impacts on ozone can be formally identified. Furthermore, by explicitly considering nonlinear signal evolution, the complete observational record can be used in the S∕N analysis, without applying piecewise linear regression and introducing arbitrary break points. The GHG-driven fingerprint of ozone changes was not statistically identifiable in either the upper- or lower-stratospheric SWOOSH data, irrespective of the signal detection method used. In the WACCM simulations of future climate change, the GHG signal is statistically identifiable between 2020 and 2030. Our findings demonstrate the importance of continued stratospheric ozone monitoring to improve estimates of the contributions of ODS and GHG forcing to global changes in stratospheric ozone.

RETRACTED: Natural resources environmental quality and economic development: Fresh analysis

Many corporate offices and industries in India are endeavouring to make their campuses progressively sustainable. Their maintainability attempt usually incorporates expanding energy efficiency of new and existing structures. There are a few buildings and equipment incorporating energy efficiency, using energy modelling technique to make them energy efficient. While energy models are broadly utilized during the design of the building system and its equipment. Further energy conservation method (ECM) is proposed to enhance building energy efficiency. The paper deals with a contextual investigation, including the utilization of a universal energy model framework to assess using energy simulation, which has undergone several remodels for a long duration. The baseline case energy simulation has been done concerning the ASHRAE 90.1-2010 modelling protocol. The paper describes the collected data, the modelling process which is determined via simulation to obtain the result that the building has potential to reach overall energy savings of 40.5% in energy costs over ASHRAE 90.1-2010 baseline design.

Energy-efficient retrofitting of buildings is a crucial aspect of achieving a specific outcome to both reduce global energy requirements and reduce carbon emissions. The Energy Analysis of buildings using BIM (Building Information Modeling) reduces energy consumption and optimise the economic benefits. This method is vital in assessing energy demand levels and the power-saving potential for cost-effective retrofitting measures. Energy Simulation is a computer-aided analysis that helps construction professionals to evaluate and increase energy efficiency through required modifications at the project planning phase. A detailed system boundary, including energy consumption reduction, cost savings, capital investment, technical change in emission behaviour, and comfort indexing together with sustainability problems, was defined. The article introduces a new BIM-based systematic approach to energy retrofitting, which comprises a whole spectrum of information acquisition, energy modelling, and software interoperability. This framework consists of identifying indicators; building envelops study, validation of research gap, identification, and energy simulation will help rehabilitation in a simple, reliable decision-making process.

LOD2 for energy simulation (LOD2ES) for CityGML: A novel level of details model for IFC-based building features extraction and energy simulation

In the Republic of Korea, the 2030 Nationally Determined Contributions aim for carbon neutrality by 2050, with the building sector targeting a 32.8% reduction in carbon emissions by 2030 compared with the 2018 baseline. To achieve these goals, significant efforts are underway to improve the energy efficiency of buildings. Building energy simulation is a standard method for evaluating energy performance as it assesses the current performance and predicts the potential contributions of energy retrofitting initiatives. However, industrial factories often lack specific energy simulation profiles, posing a challenge for accurate energy performance assessment. This case study aims to bridge this gap by investigating a detailed building profile for factory building based on the extended operational data and experimental measurements within a live factory setting. Energy simulations employing these factory-specific profiles yielded R2 values (coefficient of determination) of 98.2% and 94.1% for cooling and heating energy accuracy, respectively, when compared with the actual monthly consumption data. Additionally, simulations with these profiles demonstrated a 2.81% improvement in R2 accuracy compared to those using conventional office building profiles, particularly enhancing the precision during the cooling season. These findings highlight the effectiveness of customized profiles in building energy simulations, ensuring more precise and reliable energy efficiency assessments.

The objective of this project is to determine the total annual energy summary in terms of cost and Greenhouse Gas (GHG) emission of 16 buildings at Ryerson University (RU). In addition, the Deep Lake Water Cooling (DLWC) feasibility analysis of RU is another objective of this project in terms of total energy consumption and amount of gas emission reduction. The total audit area of RU was 86% of the total campus area. Building energy simulation program, Carrier HAP (Hourly Analysis Program), has been used to make an integrated evaluation of building energy consumption. An energy simulation involves hour-by-hour calculations for all 8,760 hours in a year. In this project, an energy audit was conducted for the 16 existing buildings to establish the base case model, "Ryerson University", to determine its annual energy consumption across all usage. There are two sources of energy used at RU. Electricity uses for lighting, plug load, miscellaneous and cooling, and remote steam is used for cooling and heating. For the base case model, total energy consumption was 251 TJ. To reduce the total energy consumption of the base case model, HVAC systems were investigated to analyze their energy-based performance and impact on the GHG emission. There is no Heat Recovery Ventilation (HRV) system coming from the investigation of HVAC system. The sensitivity analysis was conducted using HRV system with air system. By using HRV system with air system, total of 5.6% energy would be saved for cooling and 76% energy would be saved for heating of RU. The energy intensity was determined to be 1.04 GJ/m² only for 16 buildings of RU and comparatively it is lower than other universities in Canada which have a range of 1.64 GJ/m² to 2.26 GJ/m². In the DLWC system, cool lake water at 4°C was used for building air conditioning. To reduce the cooling energy costs, DLWC system was considered as an alternative chilled water source. The Rogers Business Building (RBB) already has DLWC system. For DLWC system, chilled water was served by Enwave to the RBB. According to base case analysis of the RBB with conventional chillers, the electricity consumption was 924594 kWh for RBB due to chillers. With the implementation of DLWC system for the rest of the 15 buildings, total energy saving due to cooling would be 89.2% and GHG emission reduction would be 89% for CO₂, 70% for NOx and 70.4% for SOx due to elimination of chillers.

The objective of this project is to determine the total annual energy summary in terms of cost and Greenhouse Gas (GHG) emission of 16 buildings at Ryerson University (RU). In addition, the Deep Lake Water Cooling (DLWC) feasibility analysis of RU is another objective of this project in terms of total energy consumption and amount of gas emission reduction. The total audit area of RU was 86% of the total campus area. Building energy simulation program, Carrier HAP (Hourly Analysis Program), has been used to make an integrated evaluation of building energy consumption. An energy simulation involves hour-by-hour calculations for all 8,760 hours in a year. In this project, an energy audit was conducted for the 16 existing buildings to establish the base case model, "Ryerson University", to determine its annual energy consumption across all usage. There are two sources of energy used at RU. Electricity uses for lighting, plug load, miscellaneous and cooling, and remote steam is used for cooling and heating. For the base case model, total energy consumption was 251 TJ. To reduce the total energy consumption of the base case model, HVAC systems were investigated to analyze their energy-based performance and impact on the GHG emission. There is no Heat Recovery Ventilation (HRV) system coming from the investigation of HVAC system. The sensitivity analysis was conducted using HRV system with air system. By using HRV system with air system, total of 5.6% energy would be saved for cooling and 76% energy would be saved for heating of RU. The energy intensity was determined to be 1.04 GJ/m² only for 16 buildings of RU and comparatively it is lower than other universities in Canada which have a range of 1.64 GJ/m² to 2.26 GJ/m². In the DLWC system, cool lake water at 4°C was used for building air conditioning. To reduce the cooling energy costs, DLWC system was considered as an alternative chilled water source. The Rogers Business Building (RBB) already has DLWC system. For DLWC system, chilled water was served by Enwave to the RBB. According to base case analysis of the RBB with conventional chillers, the electricity consumption was 924594 kWh for RBB due to chillers. With the implementation of DLWC system for the rest of the 15 buildings, total energy saving due to cooling would be 89.2% and GHG emission reduction would be 89% for CO₂, 70% for NOx and 70.4% for SOx due to elimination of chillers.