A NEW CLIMATE ZONING OF ETHIOPIA FOR BUILDING PERFORMANCE APPLICATIONS

Abstract

Reducing building energy demand is a mandate for design professionals today, thus emphasizing the need for climate responsiveness. This is born out of the need to minimize the negative impact on the environment while ensuring comfort and well-being of the occupants. Many countries propose energy performance standards consistent with their respective local climate and building practices. In Ethiopia, climate-responsive building research is vastly unexplored. As a result, the country lacks baseline data to help stakeholders identify climate severity, thermal performance of buildings, and energy consumption. This study aims evolve climate responsive design solutions for residential buildings of Ethiopia. The study investigates the spatio-temporal thermal severity variations in Ethiopia. The climate severity assessment is conducted using monthly climate data of temperature, relative humidity, and solar radiation variables. Further, this study provides a statistical characterization of climate based on three climate variables (temperature, humidity, and solar radiation) and identifies 20 severity based climate zones. The research evaluates the relevance of existing climate classifications (traditional agro-ecological and Köppen Geiger). Spatial assessment of thermal comfort is performed through adaptive comfort temperature (Tcomf), physiological equivalent temperature (PET) indexes, and Mahoney's method. Bioclimatic analysis is performed using Mahoney's method, and a geospatial delineation of effective bioclimatic strategies is carried out, resulting in 22 bioclimatic zones. Various studies have shown that geospatial techniques in bioclimatic design investigations relieve the designer from making the same analysis for every location. Instead, the lists of recommended design strategies and associated thermal severities of each location could be consulted from maps. A method to incorporate the impact of solar radiation on the proposed bioclimatic zones is proposed. Twenty-eight more bioclimatic sub-zones are delineated using this approach. One of the challenges in implementing bioclimatic zones has been the absence of clear zone boundaries. This issue is addressed in the current work by discretizing the bioclimatic zones to the district-level administrative boundaries. After the discretization, the final number of zones is simplified to 21. The significance of the difference in Tn and Dp among bioclimatic zones are tested using two-way ANOVA. The result shows a significant difference of Tn at [F (220, 7413) = 104.903, p < 0.0005] and Dp at [F (220, 7413) = 26.146, p < 0.0005] among climate zones.