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Bottom-Up Urban Building Energy Modelling
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Bottom-Up Urban Building Energy Modelling

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 13434

Special Issue Editors

Dr. Francesco Causone

E-Mail Website
Guest Editor
Department of Energy, Polytechnic University of Milan, Via Lambruschini 4, 20156 Milano, Italy
Interests: UBEM; occupant behaviour; zero energy buildings; thermal comfort
Special Issues, Collections and Topics in MDPI journals
Dr. Alfonso Capozzoli

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: energy data analytics; intelligent buildings; coordinated building energy management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Buildings are one of the main contributors to energy and materials used in urban areas all over the world; however, the lack of information about the energy status and energy potential of building stocks of cities is widespread. Research in urban building energy modelling (UBEM) is thus gaining momentum.

UBEM approaches are usually classified into two main categories: top-down and bottom-up. This Special Issue targets the bottom-up approach, which includes engineering, data-driven, and hybrid energy modelling, where large datasets are used to estimate the energy use of individual buildings, then aggregated to define the energy use at district and urban scales. The engineering models exploit energy balance equations, derived by single-building energy modelling (BEM), to calculate the energy use at single-building scale and then aggregate the results at district and urban scales. The data-driven approach makes it possible to connect building characteristics and other influencing parameters to the energy use by means of statistical analysis or artificial intelligence methods. Both of the approaches have advantages and limitations. Data-driven energy modelling may predict annual energy consumption and provide accurate representation of urban energy use, but it fails in simulating scenarios (e.g., retrofitting, climate change, etc.) when solely driven by historical data. Engineering-based UBEMs simulate energy demand with high spatiotemporal resolution, allowing for scenario development, but require detailed and often unknown input that may affect the reliability of aggregated results. Hybrid modelling attemps to overcome the limits of the previous approaches by integrating both of them in the analysis.

Since this is a nascent field of research, many open questions are still in need of an answer. The main open topics for research include but are not limited to (i) dataset definition and description of buildings (archetypes, prototypes), (ii) modelling of people movements and actions in buildings and in the city, (iii) different modelling for district energy systems, energy storage, and energy networks, (iv) modelling of city microclimate, green and blue infrastructures, and their integration with comfort assessment, (v) heat transfer among buildings and with the external environment, (vi) calibration and validation of models, (vii) life cycle assessment at the urban scale, and (vii) hybrid modelling approaches.

Prof. Dr. Francesco Causone
Prof. Dr. Alfonso Capozzoli
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • New data-driven UBEM tools
  • New engineering-based UBEM tools
  • Dataset definition and use
  • Archetype and prototype generation and use
  • Modelling of people’s actions and movements for UBEM
  • Use of UBEM to simulate city or district energy strategies (e.g., district energy systems, storage, etc.)
  • Modelling urban microclimate and outdoor thermal comfort assessment via UBEM
  • Calibration and validation
  • Buildings life cycle assessment at the district and urban scales

Published Papers (5 papers)

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Research

18 pages, 4715 KiB  
Article
Methodologies for Synthetic Spatial Building Stock Modelling: Data-Availability-Adapted Approaches for the Spatial Analysis of Building Stock Energy Demand
by Claudio Nägeli, Liane Thuvander, Holger Wallbaum, Rebecca Cachia, Sebastian Stortecky and Ali Hainoun
Energies 2022, 15(18), 6738; https://0-doi-org.brum.beds.ac.uk/10.3390/en15186738 - 15 Sep 2022
Cited by 3 | Viewed by 1192
Abstract
Buildings are responsible for around 30 to 40% of the energy demand and greenhouse gas (GHG) emissions in European countries. Building stock energy models (BSEMs) are an established method to assess the energy demand and environmental impact of building stocks. Spatial analysis of [...] Read more.
Buildings are responsible for around 30 to 40% of the energy demand and greenhouse gas (GHG) emissions in European countries. Building stock energy models (BSEMs) are an established method to assess the energy demand and environmental impact of building stocks. Spatial analysis of building stock energy demand has so far been limited to cases where detailed, building specific data is available. This paper introduces two approaches of using synthetic building stock energy modelling (SBSEM) to model spatially distributed synthetic building stocks based on aggregate data. The two approaches build on different types of data that are implemented and validated for two separate case studies in Ireland and Austria. The results demonstrate the feasibility of both approaches to accurately reproduce the spatial distribution of the building stocks of the two cases. Furthermore, the results demonstrate that by using a SBSEM approach, a spatial analysis for building stock energy demand can be carried out for cases where no building level data is available and how these results may be used in energy planning. Full article
(This article belongs to the Special Issue Bottom-Up Urban Building Energy Modelling)
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26 pages, 4074 KiB  
Article
Open-Source Tool for Transforming CityGML Levels of Detail
by Avichal Malhotra, Simon Raming, Jérôme Frisch and Christoph van Treeck
Energies 2021, 14(24), 8250; https://doi.org/10.3390/en14248250 - 08 Dec 2021
Cited by 7 | Viewed by 3178
Abstract
Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity [...] Read more.
Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models. Full article
(This article belongs to the Special Issue Bottom-Up Urban Building Energy Modelling)
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28 pages, 6284 KiB  
Article
A High Resolution Spatiotemporal Urban Heat Load Model for GB
by Salman Siddiqui, Mark Barrett and John Macadam
Energies 2021, 14(14), 4078; https://doi.org/10.3390/en14144078 - 06 Jul 2021
Cited by 2 | Viewed by 1952
Abstract
The decarbonisation of heating in the United Kingdom is likely to entail both the mass adoption of heat pumps and widespread development of district heating infrastructure. Estimation of the spatially disaggregated heat demand is needed for both electrical distribution network with electrified heating [...] Read more.
The decarbonisation of heating in the United Kingdom is likely to entail both the mass adoption of heat pumps and widespread development of district heating infrastructure. Estimation of the spatially disaggregated heat demand is needed for both electrical distribution network with electrified heating and for the development of district heating. The temporal variation of heat demand is important when considering the operation of district heating, thermal energy storage and electrical grid storage. The difference between the national and urban heat demands profiles will vary due to the type and occupancy of buildings leading to temporal variations which have not been widely surveyed. This paper develops a high-resolution spatiotemporal heat load model for Great Britain (GB: England, Scotland a Wales) by identifying the appropriate datasets, archetype segmentation and characterisation for the domestic and nondomestic building stock. This is applied to a thermal model and calibrated on the local scale using gas consumption statistics. The annual GB heat demand was in close agreement with other estimates and the peak demand was 219 GWth. The urban heat demand was found to have a lower peak to trough ratio than the average national demand profile. This will have important implications for the uptake of heating technologies and design of district heating. Full article
(This article belongs to the Special Issue Bottom-Up Urban Building Energy Modelling)
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30 pages, 12933 KiB  
Article
Effects of Occupants and Local Air Temperatures as Sources of Stochastic Uncertainty in District Energy System Modeling
by Martín Mosteiro-Romero and Arno Schlueter
Energies 2021, 14(8), 2295; https://0-doi-org.brum.beds.ac.uk/10.3390/en14082295 - 19 Apr 2021
Cited by 7 | Viewed by 1648
Abstract
Input uncertainty is one of the major obstacles urban building energy models (UBEM) must tackle. The aim of this paper was to quantify the effects of two of the main sources of stochastic uncertainty, namely building occupants and urban microclimate, on electrical and [...] Read more.
Input uncertainty is one of the major obstacles urban building energy models (UBEM) must tackle. The aim of this paper was to quantify the effects of two of the main sources of stochastic uncertainty, namely building occupants and urban microclimate, on electrical and thermal supply system sizing at the district scale. In order to analyze the effects of the former, three different methods of occupant modeling were implemented in a UBEM. The effects of the urban heat island on system sizing were studied through the use of measured temperature data from a weather station in the case study district compared to measured data from a national weather station. The methods developed were used to assess the sizing and costs of centralized and decentralized technologies for a case study in central Zurich, Switzerland. The choice of occupant modeling approach was found to affect the district’s total annualized costs for space heating and cooling by ±5%, whereas for the costs of electricity the variation was ±8%. Regarding outdoor temperature, the effects on the heating demands proved be negligible, however the costs of the cooling alternatives were found to vary by about 4% at the district scale due to the effect of urban climate, for individual buildings this deviation was as high as 40%. Full article
(This article belongs to the Special Issue Bottom-Up Urban Building Energy Modelling)
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17 pages, 2994 KiB  
Article
Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling
by Zhang Deng, Yixing Chen, Xiao Pan, Zhiwen Peng and Jingjing Yang
Energies 2021, 14(4), 1049; https://0-doi-org.brum.beds.ac.uk/10.3390/en14041049 - 17 Feb 2021
Cited by 20 | Viewed by 3640
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Bottom-Up Urban Building Energy Modelling)
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