Urban energy simulations using open CityGML models: A comparative analysis

Malhotra, Avichal; Shamovich, Maxim; Frisch, Jérôme; van, Christoph Treeck

doi:10.1016/j.enbuild.2021.111658

Cited by 23 publications

(5 citation statements)

References 56 publications

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“…[ 48,61 ] The data are then used together with other data sources, such as CityGML or building polygons from Open Street Map. [ 62 ] Sometimes data are enriched by public science methods, like in the case of Coloring Dresden. [ 63 ]…”

Section: Resultsmentioning

confidence: 99%

District Energy Management Systems: Key Data Points for System Integration and Related Challenges: Lessons Learned from Experts in Germany

Rehmann,

Cudok,

Schölzel

et al. 2024

Energy Tech

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District energy management systems (DEMS) offer the capability to harness the energy potential of districts. Currently, there is a lack of research describing the necessary underlying information systems. An overview of lessons learned is presented from a series of workshops with experts from industry, research, and further roles, predominately from Germany. Seven workshops discussing challenges within digitalization, data requirements, district energy system planning, common elements in district energy systems are conducted, ensuring long‐term operation and transferability, and validation of prior findings. Based on these discussions, insights into topics such as the key data and its requirements, typical distinctions in DEMS, their infrastructure, considerations in the information modeling, as well as challenges in data availability, are offered. These findings are mapped to a data value pipeline, illustrating key resources and tools needed to plan and operate DEMS. As demonstrated in this article, the requirements for DEMS highlight their complexity. Distinct categorizations, such as the ownership or heterogeneity in building and technologies, form the landscape of DEMS. The need for standardized, interoperable systems is often a contrast to the uniqueness of individual systems and their needs. Further adoption of the described concepts necessitates sustainable business models.

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Section: Resultsmentioning

confidence: 99%

District Energy Management Systems: Key Data Points for System Integration and Related Challenges: Lessons Learned from Experts in Germany

Rehmann,

Cudok,

Schölzel

et al. 2024

Energy Tech

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“…City Building Energy Saver (CityBES) [33] is an energy modelling and analysis tool for city building stock, designed to support district or city-scale efficiency programs. CityBES uses an international and open data format, CityGML [34], to simplify the input of city models, while it employs the simulation engine of EnergyPlus to estimate building energy use and potential savings from energy retrofits.…”

Section: City Building Energy Saver (Citybes)mentioning

confidence: 99%

Review of Existing Tools for the Assessment of European Building Stock Energy Demand for Space Heating and Cooling

Bottino-Leone,

Balest,

Cittati

et al. 2024

Sustainability

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There is currently a growing interest in lowering energy demand and, consequently, greenhouse gas emissions in all sectors. Several attempts by national governments to reduce energy demand are centered on the residential sector, since it accounts for a significant amount of the final energy demand. In order to estimate its energy demand and to evaluate the techno-economic effects of adopting energy efficiency and renewable energy technologies, there are comprehensive models suited for residential applications, since energy demand characteristics of the residential sector are complicated and interrelated. Based on these models, several tools are nowadays available to support designers and policymakers. These tools are designed to be user-friendly and to include the possibility to develop simulated scenarios for energy demand, production of CO2 emissions, and economic costs. The present study aims to offer an up-to-date extended overview of the most functional and widespread tools for the assessment of the current energy demand of the European building stock for space heating and cooling demand, both regarding open source and commercial licenses. Results highlighted the tools most commonly used by examining real applications, identifying their strengths and weaknesses and pinpointing the primary deficiencies for the benefit of future developers.

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“…A lack of high-quality and open-source data has increased the uncertainty in input parameters (building elements, operation, and geometry) and thus hindered the effective application of UBEMs in many parts of the world, as UBEMs mostly depend on archetype data [39]. In addition, construction year, usage, and refurbishment states are important in energy performance simulations and are often excluded in open datasets [42]. As the data are almost similar for each same class (particularly for non-geometric data), the use of archetypes may reduce variations in modeled energy use compared to the actual data [43].…”

Section: Ubems and Energy Simulationmentioning

confidence: 99%

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Hossain,

Cicco,

Bilec

2024

Buildings

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Urban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non-geometric datasets. Thus, we aimed to further elucidate the process of creating a UBEM with disparate and scarce data based on a bottom-up, physics-based approach. We focused on three typically overlooked but functionally important commercial building stocks, which are sales and shopping, healthcare facilities, and food sales and services, in the region of Pittsburgh, Pennsylvania. We harvested relevant local building information and employed photogrammetry and image processing. We created archetypes for key building types, designed 3D buildings with SketchUp, and performed an energy analysis using EnergyPlus. The average annual simulated energy use intensities (EUIs) were 528 kWh/m2, 822 kWh/m2, and 2894 kWh/m2 for sales and shopping, healthcare facilities, and food sales and services, respectively. In addition to variations found in the simulated energy use pattern among the stocks, considerable variations were observed within buildings of the same stock. About 9% and 11% errors were observed for sales and shopping and healthcare facilities when validating the simulated results with the actual data. The suggested energy conservation measures could reduce the annual EUI by 10–26% depending on the building use type. The UBEM results can assist in finding energy-efficient retrofit solutions with respect to the energy and carbon reduction goal for commercial building stocks at the city scale. The limitations highlighted may be considered for higher accuracy, and the UBEM has a high potential to integrate with urban climate and energy models, circular economy, and life cycle assessment for sustainable urban planning.

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Urban energy simulations using open CityGML models: A comparative analysis

Cited by 23 publications

References 56 publications

District Energy Management Systems: Key Data Points for System Integration and Related Challenges: Lessons Learned from Experts in Germany

District Energy Management Systems: Key Data Points for System Integration and Related Challenges: Lessons Learned from Experts in Germany

Review of Existing Tools for the Assessment of European Building Stock Energy Demand for Space Heating and Cooling

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

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