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Exploration of climate zones based on hierarchal clustering algorithm for buildings in India

Exploration of climate zones based on hierarchal clustering algorithm for buildings in India This paper proposes a new climate zoning method based on one-tier classification CDD and HDD for passive building design. The climatic zoning for buildings relied heavily on geographical parameters, deviating from actual natural-driven physics. An OpenStudio software is used to develop an energy building model, and simulations were performed for multiple geographies (30 locations) in India. The energy required to keep the area within the building at a comfortable temperature using an idealized HVAC system is also assessed. Moreover, various climatic zones were determined using climatic data and a hierarchical clustering algorithm. The variance in HDD, CDD, and the range of these zones was used to classify them with a standard deviation of 4.52, and the mean cooling load ranges from 49.17 to 62.74 kWh/m2. The highest values are found in the A1 zone, including the Eastern Ghat, Maharashtra's Deccan plateau east portion, and Gujarat. In contrast, the lowest values are found in Jharkhand (Dhanbad, Jamshedpur) and Chhattisgarh (above Korba). This work is helpful to the policymakers and gives additional insight into regional variance across the country by providing prior knowledge of energy requirements, which helps establish building architecture rules based on energy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Building Pathology and Rehabilitation Springer Journals

Exploration of climate zones based on hierarchal clustering algorithm for buildings in India

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
ISSN
2365-3159
eISSN
2365-3167
DOI
10.1007/s41024-022-00210-0
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a new climate zoning method based on one-tier classification CDD and HDD for passive building design. The climatic zoning for buildings relied heavily on geographical parameters, deviating from actual natural-driven physics. An OpenStudio software is used to develop an energy building model, and simulations were performed for multiple geographies (30 locations) in India. The energy required to keep the area within the building at a comfortable temperature using an idealized HVAC system is also assessed. Moreover, various climatic zones were determined using climatic data and a hierarchical clustering algorithm. The variance in HDD, CDD, and the range of these zones was used to classify them with a standard deviation of 4.52, and the mean cooling load ranges from 49.17 to 62.74 kWh/m2. The highest values are found in the A1 zone, including the Eastern Ghat, Maharashtra's Deccan plateau east portion, and Gujarat. In contrast, the lowest values are found in Jharkhand (Dhanbad, Jamshedpur) and Chhattisgarh (above Korba). This work is helpful to the policymakers and gives additional insight into regional variance across the country by providing prior knowledge of energy requirements, which helps establish building architecture rules based on energy.

Journal

Journal of Building Pathology and Rehabilitation – Springer Journals

Published: Dec 1, 2022

Keywords: Passive buildings; Climate-zoning; Hierarchical clustering; Green buildings; Energy savings

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