Dr. Miguel Martin Postdoctoral Research Fellow
M.Martin@tudelft.nl

This paper describes a firsthand and open dataset comprising weather data collected from four street-level stations and microscale thermal images captured by a single infrared thermal camera during an extreme heat event in late August 2024 in Pittsburgh, United States. The weather data includes air temperature, relative humidity, wind speed and direction, and rainfall, measured at a height of 2 meters above the ground. From microscale thermal images, it is possible to assess the temperatures of built-up surfaces within a street canyon on a university campus, including a road, sidewalks, and building façades. Other factors that contribute to or mitigate urban overheating, such as waste heat emissions, traffic, and vegetation, can also be analyzed using the microscale thermal images. The weather data and microscale thermal images are publicly accessible in the 4TU.ResearchData repository under the CC BY 4.0 license. A Python library was developed to extract and process the data, particularly microscale thermal images, and is publicly available via the PIP package installer.

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This paper studies heat fluxes from contributors and mitigators of urban heat islands using thermal images and weather data. Thermal images were collected by a rooftop observatory between November 2021 and April 2022. Over the same period, weather stations were operating at several locations on a university campus in Singapore. From collected data, a method was defined to estimate sensible and latent heat fluxes from building facades, vegetation, and traffic. Before analyzing heat fluxes using the method, thermal images were calibrated against measurements made with contact surface sensors. Results show that the method can be used to study heat fluxes with a higher temporal resolution at the neighbourhood scale than any other technique using thermal images collected by a satellite. Heat fluxes can also be analyzed over a longer period while considering urban morphology with a higher fidelity than these estimated using most urban climate models. However, as heat fluxes are directly calculated from measurements, the method is not able to forecast the impact of certain elements in the built environment on urban heat islands. In the future, this limitation can be overcome if heat fluxes assessed by the method are used to train and test data driven models.

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This work presents a study on the characterization of the air-conditioning (AC) usage pattern of non-residential buildings from longitudinal thermal images collected at the urban scale. The operational pattern of two different air-conditioning systems (water-cooled systems operating on a pre-set schedule and window AC units operated by the occupants) are studied from the thermal images. It is observed that for the water-cooled system, the difference between the rate of change of the window and wall temperature can be used to extract the operational pattern. While, in the case of the window AC units, wavelet transform of the AC unit temperature is used to extract the frequency and time domain information of the AC unit operation. The results of the analysis are compared against the indoor temperature sensors installed in the office spaces of the building. This forms one of the first few studies on the operational behavior of HVAC systems for non-residential buildings using the longitudinal thermal imaging technique. The output from this study can be used to better understand the operational and occupant behavior, without requiring to deploy a large array of sensors in the building space.

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The paper presents a review on major contributions in infrared thermography to study the built environment at multiple scales. To elaborate the review, hundreds of studies conducted between the 1980s and 2020s were first selected based on their relevance to the scope. Afterward, the most relevant contributions were classified and chronologically sorted. From the classification, it is observed that most reviewed studies were conducted to evaluate the thermal performance of buildings or detect their defects using images collected by an infrared camera. At the same time, a considerable number of studies used thermal images obtained by a satellite to observe the urban heat island effect. Despite the important number of contributions in infrared thermography at multiple scales of the built environment, three main research gaps or opportunities can be identified in the literature. First, it would be possible to perform a more detailed analysis of urban heat fluxes using thermal images collected at multiple scales. Then, thermal images collected by a mounted or handheld infrared camera could be used to create building energy models. Finally, better visualization tools would be developed to monitor a city’s energy use and improve its sustainability if thermal images were integrated into Internet-of-Things and digital twin platforms.

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This paper introduces a physically-based model to simulate interactions between a building and its outdoor conditions at the urban microscale. In the model, the building is simulated with EnergyPlus while its outdoor conditions are assessed from OpenFOAM. Furthermore, the model simulates the street pavement and surrounding buildings based on the lumped thermal theory. All components of the model are quasi-dynamically co-simulated. Through simulations of the model, waste heat releases from a cooling system can be observed with a higher resolution than this achieved by most urban microclimate models in the literature. Unlike several methods in the literature, the model evaluates a countermeasure to urban heat islands by considering direct and indirect effects simultaneously. To validate the model, measurements were collected during a field experiment in an university campus. Comparing measurements to estimates, the model seems to properly approximate the outdoor temperature and the air motion. However, a discrepancy is observed between estimates and measurements of the surface temperature. The discrepancy could be minimised if a better consideration of the net-longwave radiation was possible when co-simulating EnergyPlus. After some improvements, the model could become a support tool to mitigate urban heat islands and climate change in different regions of the world.

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An urban microclimate model integrated with the mesoscale, local scale, microscale and building scale models is presented for the urban thermal environment and urban heat island (UHI) mitigation study. This integrated multi-scale and multi-physics urban microclimate model takes the impacts on the urban microclimate of multiple physical processes of regional climate, urban climate, building and pavement material properties, and anthropogenic heat into account. This objective is achieved by coupling weather and energy models, the weather research forecasting (WRF), OpenFOAM and EnergyPlus. This study presents a method of distributing initial and boundary conditions from mesoscale into microscale through the integration of building thermal behaviour and microclimate. We further examine the integrated model by taking the Kent Ridge campus of National University of Singapore (NUS) as a test case. The preliminary application focuses on the assessment of weather conditions at the local scale and the feasibility of using mesoscale WRF outputs as inputs for the CFD model.

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The urban heat island (UHI) phenomenon has become a concern in many major cities worldwide, as high summer temperatures and poor wind flow can have negative impacts on city dwellers, particularly increasing energy demand for artificial cooling. This paper showcases how an Integrated Multi-scale Environmental Urban Model (IMEUM) can be employed to support planners, architects and engineers to assess the combined impacts of the UHI phenomenon and rising global temperatures due to climate change. IMEUM concept derives from downscaling environmental models from global scale (25 km) to mesoscale (1 km) and city scale (100 m). Hence, this paper showcases a computationally efficient method which couples multi-scale atmospheric models with statistical model to estimate weather parameters. Developed under Singapore context, IMEUM can be utilized to incorporate appropriate UHI mitigation measures upfront in design process, consecutively bridging the gap between global and building scale. This paper also includes calibration of the IMEUM output using observations from ground sensors. Furthermore, by using case study of a hypothetical office building, this paper showcases how the IMEUM output can be fully converted into a localized weather data file for cooling load simulation. IMEUM is currently being developed further into integrated quantitative urban environment simulation tool (QUEST), which can be used to test the immediate microclimatic impact of development plans and assess their long term impacts under future climate change scenarios.

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This paper gives practical information related to the use of simplified EnergyPlus models and its impact on estimates of the cooling demand, especially when EnergyPlus models are coupled with an urban canopy model. By simplified models, it is referred to shoebox as well as multi-floor models. For the interest of climatologists, their influence on estimates of urban temperature and specific humidity is also studied. To explore divergences between the use of detailed and simplified EnergyPlus models, a typical office area in Singapore is selected as a case study. As a result, it is observed that the use of multi-floor models provides estimates of the cooling demand and urban microclimatic conditions which achieve the best agreement with these assessed from detailed models. On the other hand, less time is required to run a shoebox than a multi-floor model. Although these outcomes are inferred from an excellent case study, further research must be conducted on several types of urban areas to elaborate a general theory.

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In this paper, we provide further evidence of the reliability of a lumped parameter urban canopy model coupled with an EnergyPlus building energy model to estimate urban temperatures. In a previous paper, we presented a preliminary validation of the model using data measured at Masdar Institute, Abu Dhabi, United Arab Emirates. At present, we conduct a more comprehensive validation based on BUBBLE experimental data measured in the Sperrstrasse, a street canyon located in Basel downtown, Switzerland. To extend the coupled scheme for the Sperrstrasse and future work, we developed methods for approximating waste heat releases generated by a heating system, anthropogenic heat gains created by traffic, and direct normal solar irradiance. Based on a baseline coupled scheme model for the Sperrstrasse, we evaluate the sensitivity of urban temperature estimates to ±20% variations of each parameter. We finally show, through Monte-Carlo analysis, that the coupled scheme can achieve satisfactory accuracy in a dense Central European city.

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In this paper, we describe a lumped thermal parameter model coupled with an EnergyPlus model used for estimating temperature and specific humidity in the near-surface urban environment. Estimations made by the model are compared to measurements obtained from data loggers installed in an urban canyon of Masdar Institute (Abu Dhabi). Based on these comparisons, we first evaluate the most likely ratios of heat released into the urban canyon by a building air handling unit and the wind tower that produces adiabatically cooled air. Next, we analyze three specific case studies to obtain a local estimate of the accuracy that is reached by the coupled scheme. To estimate its global precision, we perform a sensitivity and Monte-Carlo analysis over the most likely ratios of heat emitted by the air handling unit and the wind tower. Although validation in a dense downtown is still lacking and will be undertaken in the future, this study suggests that urban temperature and humidity can be estimated with an acceptable accuracy under moderate waste heat releases and anthropogenic heat gains.

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This paper suggests a method to simulate interactions between buildings and their outdoor conditions at the city-scale using a coupled scheme whose physical parameters are entirely assessed from data of the indoor and outdoor built environment. The coupled scheme consists of a reduced order building energy model and a single layer urban canopy model. In a previous study, it was proven that physical parameters of a single layer urban canopy model can be assessed using measurements of the outdoor temperature and humidity in a street canyon. For the coupled scheme to be fully data driven, the next step is to demonstrate that the reduced order building energy model can estimate the cooling consumption and exterior wall surface temperature in good agreement with measurements or simulated data after being trained using machine learning. Indeed, results show that a multi objective genetic algorithm can find values for physical parameters of the reduced order building energy model. Estimates of the cooling consumption and exterior wall surface temperature provided by the trained model achieve a CV-RMSE below 10% and a RMSE lower than 2.5 Kelvin, respectively, with respect to data generated from EnergyPlus. The last step towards a full data driven coupled scheme for city-scale simulations would be to iteratively train the reduce order building energy model with the single layer urban canopy model and show the convergence and accuracy of their respective outputs.

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This paper describes a data driven urban canopy model that can be coupled with detailed building energy models. The data driven model is used to assess the outdoor air temperature and humidity in a street canyon considering as inputs weather conditions at the atmospheric layer, the surface temperature of surrounding building facades and the street, and the heat released from the use of air-conditioning. Predictions made by the model were tested using measurements of the outdoor air temperature and humidity collected between April and August 2019 in Singapore. Results show that the model estimates the outdoor air temperature with a similar accuracy than others that were validated using the same input and test data, while providing estimates with a higher temporal resolution and considering urban morphology with a higher fidelity. They also demonstrate that the model can predict the impact of waste heat releases and cool pavement on the outdoor air temperature and building energy consumption. In the future, vegetation could be considered as an input of the model if the land surface temperature is measured using an infrared thermal camera. Another improvement would be to define weather conditions at the atmospheric layer from rooftop measurements or a climate model.

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Urban Building Energy Models (UBEMs) often ignore interactions between buildings and their outdoor conditions. To determine their importance when calibrating an UBEM, this study performed various statistical analyses using a coupling between detailed building energy models and a data driven urban canopy model. Results of statistical analyses suggest that the sensitivity of an UBEM with respect to uncertain parameters is affected by interactions between buildings and their outdoor conditions. They also reduce the probability to reach requirements for calibration. The reason is that interactions between buildings and their outdoor conditions are an important factor driving the goodness-of-fit of an UBEM.

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This study evaluates variations in the surface temperature of street pavements caused by retro-reflective glass when applied on highly-glazed facades of high-rise buildings located in Singapore and Tokyo. To accomplish this, simulations are conducted on one experimental site situated in business areas of Singapore and another in the city centre of Tokyo. Incoming solar radiations on street pavements and heat transfers affecting their surface temperature are among the physical phenomena being simulated. As a result of the data analysis, it appears that the use of accordion retro-reflective glass on highly-glazed façades could noticeably reduce the surface temperature of street pavements in business areas of both Singapore and Tokyo, compared with other types of glass. A more significant decrease in the surface temperature can be expected in the experimental site of Tokyo, particularly near the summer solstice. On the other hand, the design optimisation of accordion retro-reflective glass seems to be more effortlessly achieved in Singapore. These outcomes represent an encouraging step towards the reduction of building energy use, as well as the mitigation of the Urban Heat Island effect, from the use of retroreflective glass façades in business areas of different cities experiencing various climates.

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