Assessment of the Spatial Configuration Pattern in Tiruchirappalli City for Energy Studies through Generative Urban Prototype Models: A Case for Warm and Humid Climate
Keywords:
local climatic zones, Image classification, urban morphology, cooling energy consumption, machine learning, climate change
Abstract
Developing countries with complex urban spatial configurations strive to control urbanization and its impact on energy consumption. The current study has used Tiruchirappalli city in India as a study area to demonstrate the impact on cooling energy consumption by complex urban spatial configurations. To comprehend the complexity, sixty-five urban prototypes were generated through permutation and combination using local climatic zones scheme. The image-based binary classification model was used to categorize the morphologies in the city. The study aims to investigate the cooling energy consumption of a heterogeneous urban spatial configuration through prototype models. The urban prototypes were grouped using the unsupervised machine learning approach. The validation for the prototypes was conducted through the RMSE method, and the errors lie between 0.45 and 0.68. The results indicated that increasing the green cover ratio on the combination of high and mid-rise spatial configurations is inef fective in reducing the cooling energy. In contrast, the combination of low-rise and mid-rise spatial configurations consumed less energy for air-conditioning when the green cover ratio was increased. The results conclude that the combination of high-rise with open low-rise spatial configuration is unsuitable for warm and humid climate. The high frequency of the cooling energy was between 120Gjs to 250Gjs which explains that the complexity of the spatial configuration in the city helps to reduce the energy utilized for air conditioning. This research aids planners and energy policymakers in the decision-making process of city spatial planning.
References
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Nagpal, S., Hanson, J., & Reinhart, C. (2019). A framework for using calibrated campus-wide building energy models for continuous planning and greenhouse gas emissions reduction tracking. Applied Energy, 241, 82–97. https://doi.org/10.1016/j.apenergy.2019.03.010
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Nastran, M., Kobal, M., & Eler, K. (2019). Urban heat islands in relation to green land use in European cities. Urban Forestry and Urban Greening, 37(January 2018), 33–41. https://doi.org/10.1016/j.ufug.2018.01.008
Natanian, J., & Auer, T. (2020). Beyond nearly zero energy urban design: A holistic microclimatic energy and environmental quality evaluation workflow. Sustainable Cities and Society, 56(February), 102094. https://doi.org/10.1016/j.scs.2020.102094
Nik, V. M. (2016). Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs). Applied Energy, 177, 204–226. https://doi.org/10.1016/J.APENERGY.2016.05.107
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