This paper aims to find the impact of geometric parameters on the energy performance of buildings, to using them to identify types regarding major geometric characteristics of a target area. Conventional approaches to control energy efficiency of buildings mainly focus on materials and capacity of insulation, but rarely consider urban and building geometries. By examining energy impacts on urban blocks by urban geometric forms, this paper seeks to identify urban geometric types and energy patterns on urban blocks. To achieve the aims of this study, this paper follows two steps: First, significant indicators for analyzing energy performance are identified in urban geometries; second, the types that capture urban geometry of a real city are categorized. As a result, as a reference for urban planning and design, the paper identifies 13 types that represent the characteristics of urban geometries regarding energy performance. The geometric indicators are carefully measured and their significance to energy performance of buildings is examined through regression analysis. According to these indicators, the 13 types are categorized using a hierarchical clustering algorithm, a machine learning method. Additionally, the 13 types are discussed for implementation as references in urban planning and design, particularly in block planning for a city.