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Open AccessJournal ArticleDOI

Urban Building Energy Modeling (UBEM) Tools: A State-of-the-Art Review of bottom-up physics-based approaches.

TLDR
In this paper, the authors present a review of the main bottom-up physics-based UBEM tools, comparing them from a user-oriented perspective, focusing on the required inputs, the reported outputs, the exploited workflow, the applicability of each tool, and the potential users.
Abstract
Regulations corroborate the importance of retrofitting existing building stocks or constructing new energy efficient district. There is, thus, a need for modeling tools to evaluate energy scenarios to better manage and design cities, and numerous methodologies and tools have been developed. Among them, Urban Building Energy Modeling (UBEM) tools allow the energy simulation of buildings at large scales. Choosing an appropriate UBEM tool, balancing the level of complexity, accuracy, usability, and computing needs, remains a challenge for users. The review focuses on the main bottom-up physics-based UBEM tools, comparing them from a user-oriented perspective. Five categories are used: (i) the required inputs, (ii) the reported outputs, (iii) the exploited workflow, (iv) the applicability of each tool, and (v) the potential users. Moreover, a critical discussion is proposed focusing on interests and trends in research and development. The results highlighted major differences between UBEM tools that must be considered to choose the proper one for an application. Barriers of adoption of UBEM tools include the needs of a standardized ontology, a common three dimensional city model, a standard procedure to collect data, and a standard set of test cases. This feeds into future development of UBEM tools to support cities' sustainability goals.

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Citations
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Journal ArticleDOI

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

TL;DR: In this paper , a study on data-driven probabilistic machine learning (ML) techniques and their real-time applications to smart energy systems and networks was conducted to highlight the urgency of this area of research.
Journal ArticleDOI

Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions

TL;DR: In this article , the authors introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies.
Journal ArticleDOI

Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

TL;DR: Wang et al. as mentioned in this paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable, which can be easily applied to other cities in China.
Journal ArticleDOI

Data acquisition for urban building energy modeling: A review

TL;DR: In this article , appropriate data acquisition approaches for UBEM are reviewed for four data inputs, learning from both building science and other disciplines such as geography, transportation and computer science.
Journal ArticleDOI

Times series forecasting for urban building energy consumption based on graph convolutional network

TL;DR: Wang et al. as discussed by the authors proposed a novel data-driven UBEN synthesizing the solar-based building interdependency and spatio-temporal graph convolutional network (ST-GCN) algorithm.
References
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Journal ArticleDOI

Contrasting the capabilities of building energy performance simulation programs

TL;DR: In this paper, a comparison of the features and capabilities of twenty major building energy simulation programs is presented, based on information provided by the program developers in the following categories: general modeling features; zone loads; building envelope and daylighting and solar; infiltration, ventilation and multizone airflow; renewable energy systems; electrical systems and equipment; HVAC systems; HVC equipment; environmental emissions; economic evaluation; climate data availability, results reporting; validation; and user interface, links to other programs, and availability.
Journal ArticleDOI

Modeling of end-use energy consumption in the residential sector: A review of modeling techniques

TL;DR: In this paper, the authors provide an up-to-date review of the various modeling techniques used for modeling residential sector energy consumption, focusing on the strengths, shortcomings and purposes.
Journal ArticleDOI

Modelling radiation fluxes in simple and complex environments—application of the RayMan model

TL;DR: The physical basis of the RayMan model, which simulates the short- and long-wave radiation flux densities from the three-dimensional surroundings in simple and complex environments, is presented.
Journal ArticleDOI

A review of bottom-up building stock models for energy consumption in the residential sector

TL;DR: In this paper, bottom-up and top-down building stock models have been used to estimate the baseline energy demand of the existing building stock, explore the technical and economic effects of different CO2 emission reduction strategies over time, including the impact of new technologies, and identify the effect of emission reduction strategy on indoor environmental quality.
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