Calculating urban heat demands: An analysis of two modelling approaches and remote sensing for input data and validation

Dochev, Ivan; Gorzalka, Philip; Weiler, Verena; Schmiedt, Jacob Estevam; Linkiewicz, Magdalena; Eicker, Ursula; Hoffschmidt, Bernhard; Peters, Irene; Schröter, Bastian

doi:10.1016/j.enbuild.2020.110378

Cited by 15 publications

(12 citation statements)

References 31 publications

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“…The success of step 6 was also affected by tree coverage: many windows could not be detected. Still, the recognised windows fit well to reality on many façades and the results outmatch usual window-wall ratio assumptions (Dochev et al (2020)). The outcome of the process is the CityGML model visualised in Figure 3.…”

Section: D Geometry Generationsupporting

confidence: 67%

“…This is caused by the heterogeneity of heating energy sources as well as by data protection regulations and is a strong motivation for applying UBEM. In a previous study on the quarter, Dochev et al (2020) created a dataset to compare different UBEM methods. From the 387 buildings and building parts in the investigated area, 279 are classified as "residential" in the cadastre.…”

Section: Case Study Quartermentioning

confidence: 99%

“…Figure 2 shows the distribution into age classes according to Berlin's "Geoportal" (Senatsverwaltung für Stadtentwicklung und Wohnen (1993)) for buildings built before 1993 and to historic aerial imagery for the rest. To achieve correspondence with the TABULA typology age classes, relevant for heat demand estimation, the middle of each age class in the Geoportal was chosen as the building year of construction as in Dochev et al (2020Dochev et al ( ). 1870Dochev et al ( -1899Dochev et al ( : 42 % 1900Dochev et al ( -1918Dochev et al ( : 39 % 1946Dochev et al ( -1961Dochev et al ( : 7 % 1962Dochev et al ( -1974Dochev et al ( : 6 % 1975Dochev et al ( -1992Dochev et al ( : 3 % 1993Dochev et al ( -2000…”

Section: Case Study Quartermentioning

confidence: 99%

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Collecting data for urban building energy modelling by remote sensing and machine learning

Gorzalka¹,

Garbasevschi²,

Schmiedt³

et al. 2021

Building Simulation Conference Proceedings

Self Cite

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High-quality data on the investigated area is crucial for modelling urban building energy demands, but its availability is often insufficient. We present an approach to acquire (i) building geometries, (ii) their ages, and (iii) their retrofit states. It consists of creating a 3D model from aerial imagery, determining building ages through machine learning, generating a simulation model based on open-source tools, and assessing retrofit states by comparing simulated temperatures with infrared thermography (IRT) measurements.The demonstration on a case study quarter in Berlin shows that heat demand results are comparable to other tools. Using machine learning is already wellsuited to close knowledge gaps regarding building ages. However, retrofit state assessment using IRT was unsatisfactory due to insufficient measurement accuracy and is envisaged for improvement in future research, along with a validation of the approach. Key Innovations• Combined workflow using remote sensing, machine learning, and data enrichment tools to collect building stock data • Application of an additional, Modelica-based simulation method to the case study quarter used by Dochev et al. ( 2020) who compare two urban building energy modelling approaches Practical ImplicationsHigh-quality urban building energy models may be generated even in case of limited input data availability prior to the study, but some open issues exists.

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Section: D Geometry Generationsupporting

confidence: 67%

Section: Case Study Quartermentioning

confidence: 99%

Section: Case Study Quartermentioning

confidence: 99%

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Collecting data for urban building energy modelling by remote sensing and machine learning

Gorzalka¹,

Garbasevschi²,

Schmiedt³

et al. 2021

Building Simulation Conference Proceedings

Self Cite

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“…In order to process the building database by adding parameters capable of describing the urban morphology, it is necessary to use tools able to collect input data and manage city building dataset. The most common are CityGML model which uses a standardized geometrical format and a geographical information system (GIS) [18]. With CityGML it is possible to create and design 3D city models using an open standard format to support the energy simulation at city level [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

Todeschi¹,

Boghetti

Kämpf

et al. 2021

Sustainability

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Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

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“…In recent years, remote sensing technology and processing of satellite images have been increasingly applied to tackle various challenging problems in a wide span of domains including agriculture [11][12][13], natural hazard prevention [14][15][16][17], civil engineering [18][19][20], and environmental engineering [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

Hoang

2021

Computational Intelligence and Neuroscience

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Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel-2 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management.

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Calculating urban heat demands: An analysis of two modelling approaches and remote sensing for input data and validation

Cited by 15 publications

References 31 publications

Collecting data for urban building energy modelling by remote sensing and machine learning

Collecting data for urban building energy modelling by remote sensing and machine learning

Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

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