GIS-based approach to identify climatic zoning: A hierarchical clustering on principal component analysis

Abstract

In tropical environments, the design of bioclimatic houses adapted to their environment is a crucial issue when considering comfort and limiting energy needs. A preliminary part of such design is an accurate knowledge of the climatic conditions in each region of the studied territory. The objective of this paper is to propose climatic zoning from a database of 47 meteorological stations in Madagascar by investigating hierarchical clustering on principal components. Then, theses results are combined with a spatial interpolation using a Geographic Information System approach. This step allows us to define three climatic zones corresponding to dry, humid and highland zones. These results make it possible to define standard meteorological files that are used to evaluate the thermal performance of traditional Malagasy houses. Regardless of the type of house and the areas considered, the percentage of comfort, according to Givoni bioclimatic chart, varies from an average value of 20 % to 70 % without ventilation and with an air velocity of 1 m/s, respectively. It can be concluded that Madagascar's traditional habitat has adapted over time to the constraints of its environment.

Climatic zoning for the building thermal design in China's rural areas

“…The first task in defining a climatic zoning map is the acquisition of meteorological data. 16 In this study, recent weather datasets (2004-2018) with the last version of the Typical Meteorological Year hourly weather data files for 387 selected locations were used. 17 The degree day index could be seen as one of the most practical and simple indices in determining the required energy for providing comfort climate is.…”

“…The inverse distance weighting (IDW) method is well suited to the dataset in this study and demonstrates the advantage of usually being both sufficient and appropriate. 16,25 IDW method takes the distance between the estimated point and sample point as the weight to carry out the weighted average, the closer the estimated point to the estimated sample point is, the greater the weight is given. 26 The generic formulation is expressed in equation (3), 27 where z i is the sample value at point i, z x,y is the point to be estimated, d x,y is the distance from the sample point to the estimated point, and the variable b called the exponent value improves the accuracy of the IDW interpolator between the measured and estimated data.…”

Carbon Emission Governance Zones at the County Level to Promote Sustainable Development

“…The PSR and its expansion framework have been widely used in research on city carbon emission governance [33] and have proven advantages in building indicator systems and developing political goals related to environmental issues [30]. For geographical zoning, the classical zoning methods can be categorized as clustering analysis [34][35][36], spatial auto-correlation [37,38], and analytic hierarchy process (AHP) [39,40]. More suitable for governance-oriented zoning, clustering analysis is used mostly for multi-factor integrated zoning because it can reflect the differences and convergences among regions by integrating many governance elements.…”

“…More suitable for governance-oriented zoning, clustering analysis is used mostly for multi-factor integrated zoning because it can reflect the differences and convergences among regions by integrating many governance elements. The zoning method is widely used in research on geographical zoning, such as geological zoning [41,42], climate zoning [35,43,44], and ecological functional zoning [45][46][47][48], but rarely used in research on carbon emission governance. Moreover, most zoning research has focused on reflecting the external differences in the carbon emissions of regions directly [49] but has disregarded the differences in the internal motivations.…”

Validation of the climatic zoning defined by ASHRAE standard 169-2013

“…It has the advantage of being simple to calculate as it is based on a single parameter, however building energy efficiency is influenced by several weather parameters (Clarke, 2001) which may reduce the validity of the degree-day method. Although less common than the degree-days method, building energy simulation (BES) is becoming more widely used in climatic zoning applications (Praene et al, 2019;Verichev and Carpio, 2019). It has been adopted in 8% of cases covered in the review.…”