scholarly journals Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1049
Author(s):  
Zhang Deng ◽  
Yixing Chen ◽  
Xiao Pan ◽  
Zhiwen Peng ◽  
Jingjing Yang
Keyword(s):  
Large Scale ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Energy Modeling ◽  
Office Buildings ◽  
Building Energy Modeling ◽  
Downtown Area ◽  
Point Of Interest

Urban building energy modeling (UBEM) is arousing interest in building energy modeling, which requires a large building dataset as an input. Building use is a critical parameter to infer archetype buildings for UBEM. This paper presented a case study to determine building use for city-scale buildings by integrating the Geographic Information System (GIS) based point-of-interest (POI) and community boundary datasets. A total of 68,966 building footprints, 281,767 POI data, and 3367 community boundaries were collected for Changsha, China. The primary building use was determined when a building was inside a community boundary (i.e., hospital or residential boundary) or the building contained POI data with main attributes (i.e., hotel or office building). Clustering analysis was used to divide buildings into sub-types for better energy performance evaluation. The method successfully identified building uses for 47,428 buildings among 68,966 building footprints, including 34,401 residential buildings, 1039 office buildings, 141 shopping malls, and 932 hotels. A validation process was carried out for 7895 buildings in the downtown area, which showed an overall accuracy rate of 86%. A UBEM case study for 243 office buildings in the downtown area was developed with the information identified from the POI and community boundary datasets. The proposed building use determination method can be easily applied to other cities. We will integrate the historical aerial imagery to determine the year of construction for a large scale of buildings in the future.

scholarly journals An open source analysis framework for large-scale building energy modeling

2020 ◽  
Vol 13 (5) ◽  
pp. 487-500
Author(s):  
Brian L. Ball ◽  
Nicholas Long ◽  
Katherine Fleming ◽  
Chris Balbach ◽  
Phylroy Lopez
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Building Energy ◽  
Energy Modeling ◽  
Source Analysis ◽  
Analysis Framework ◽  
Building Energy Modeling

Urban building energy modeling considering the heterogeneity of HVAC system stock: A case study on Japanese office building stock

2020 ◽  
Vol 207 ◽  
pp. 109590
Author(s):  
Bumjoon Kim ◽  
Yohei Yamaguchi ◽  
Shun Kimura ◽  
Yumei Ko ◽  
Kosuke Ikeda ◽  
...  
Keyword(s):  
Building Energy ◽  
Energy Modeling ◽  
Office Building ◽  
Hvac System ◽  
Building Energy Modeling ◽  
Building Stock

scholarly journals Advancing urban analytics for energy transitions: Data-driven strategic planning for citywide building retrofitting

2020 ◽  
Author(s):  
◽  
Oleksii Pasichnyi
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Large Scale ◽  
Building Energy ◽  
Energy Performance ◽  
Energy Data ◽  
Modelling Framework ◽  
Energy Transitions ◽  
Energy Modelling

Decarbonisation of the building stock is essential for energy transitions towards climate-neutral cities in Sweden, Europe and globally. Meeting 1.5°C scenarios is only possible through collaborative efforts by all relevant stakeholders — building owners, housing associations, energy installation companies, city authorities, energy utilities and, ultimately, citizens. These stakeholders are driven by different interests and goals. Many win-win solutions are not implemented due to lack of information, transparency and trust about current building energy performance and available interventions, ranging from city-wide policies to single building energy service contracts. The emergence of big data in the building and energy sectors allows this challenge to be addressed through new types of analytical services based on enriched data, urban energy models, machine learning algorithms and interactive visualisations as important enablers for decision-makers on different levels. The overall aim of this thesis was to advance urban analytics in the building energy domain. Specific objectives were to: (1) develop and demonstrate an urban building energy modelling framework for strategic planning of large-scale building energy retrofitting; (2) investigate the interconnection between quality and applications of urban building energy data; and (3) explore how urban analytics can be integrated into decision-making for energy transitions in cities. Objectives 1 and 2 were pursued within a single case study based on continuous collaboration with local stakeholders in the city of Stockholm, Sweden. Objective 3 was addressed within a multiple case study on participatory modelling for strategic energy planning in two cities, Niš, Serbia, and Stockholm. A transdisciplinary research strategy was applied throughout. A new urban building energy modelling framework was developed and demonstrated for the case of Stockholm. This framework utilises high-resolution building energy data to identify buildings and retrofitting measures with the highest potential, assess the change in total energy demand from large-scale retrofitting and explore its impact on the supply side. Growing use of energy performance certificate (EPC) data and increasing requirements on data quality were identified in a systematic mapping of EPC applications combined with assessment of EPC data quality for Stockholm. Continuity of data collaborations and interactivity of new analytical tools were identified as important factors for better integration of urban analytics into decision-making on energy transitions in cities.

scholarly journals Building Information Modeling-Based Building Energy Modeling: Investigation of Interoperability and Simulation Results

2020 ◽  
Vol 6 ◽  
Author(s):  
Mohamed H. Elnabawi
Keyword(s):  
Large Scale ◽  
Building Information Modeling ◽  
Data Transfer ◽  
Building Design ◽  
Building Energy ◽  
Energy Modeling ◽  
Low Energy ◽  
Information Modeling ◽  
Building Energy Modeling ◽  
Building Information

There is increasing need to apply building information modeling (BIM) to low energy buildings, this includes building energy modeling (BEM). If a building energy model can be flawlessly generated from a BIM model, the energy simulation process can be better integrated within the design, can be more competent, and timesaving. However, concerns about both the reliability and integrity of the data transfer process and the interoperability between the BIM and BEM prevent any implementation of BIM-based energy modeling on a large scale. This study addresses the accuracy and integrity of BIM-based energy modeling by investigating how well Autodesk's Revit (BIM), in conjunction with two of the most used energy modeling programs (BEM) known as DesignBuilder and Virtual Environment (IES-ve), were integrated in terms of interoperability, including location and weather files, geometry, construction and materials, thermal zones, occupancy operating schedules, and HVAC systems. All misrepresented data during the interoperability process were identified, followed by benchmarking between the BIM-based energy modeling simulation outcomes and the actual energy consumption of the case study, to assess the reliability of the process. The investigation has revealed a number of interoperability issues regarding the BIM data input and BEM data interpretation. Overall, BIM-based energy modeling proved to be a promising tool for sustainable and low energy building design, however, the BIM to BEM process is a non-standardized method of producing building energy models as it varies from one modeler to another, and the BIM to BEM process. All these might slow down any possible application for the process and might cause some uncertainties for the professionals in the field applying it.

scholarly journals Urban building energy modeling considering the heterogeneity of HVAC system stock: A case study on Japanese office building stock

2019 ◽  
Vol 199 ◽  
pp. 547-561 ◽  
Author(s):  
Bumjoon Kim ◽  
Yohei Yamaguchi ◽  
Shun Kimura ◽  
Yumei Ko ◽  
Kosuke Ikeda ◽  
...  
Keyword(s):  
Building Energy ◽  
Energy Modeling ◽  
Office Building ◽  
Hvac System ◽  
Building Energy Modeling ◽  
Building Stock

scholarly journals Development and Verification of Prototypical Office Buildings Models Using the National Building Energy Consumption Survey in Korea

2021 ◽  
Vol 13 (7) ◽  
pp. 3611
Author(s):  
Hye-Jin Kim ◽  
Do-Young Choi ◽  
Donghyun Seo
Keyword(s):  
Energy Consumption ◽  
Energy Policy ◽  
Policy Development ◽  
Large Scale ◽  
Residential Buildings ◽  
Residential Building ◽  
Building Energy ◽  
Office Buildings ◽  
Policy Enforcement ◽  
Building Stock

In the early 2000s, the Korean government mandated the construction of only zero-energy residential buildings by 2025 and for non-residential buildings from 2030. Two decades since the start of building energy policy enforcement, Korean experts believe that it is time to evaluate its impact. However, few studies have systematically and extensively examined the energy consumption characteristics of the non-residential building stock. In this study, a framework development is implemented for defining non-residential prototypical office buildings based on Korea’s first large-scale non-residential building survey result from the Korea Energy Economics Institute (KEEI). Then, a detailed building energy model of the defined prototypical building is constructed to verify the model’s energy estimation against observed energy consumption. As an application of the model, a case study for energy policy evaluation utilizing the constructed prototypical building model is presented. Every researcher and county may have their own circumstances when gathering definition data. However, by using the best available representative data, this suggested framework may result in informed decisions regarding energy policy development and evaluation. In addition, the mitigation of greenhouse gases from buildings may be expedited.

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