Avaliação do consumo de energia elétrica de edificações de escritório e sua correlação com as decisões de projeto

Abstract

In countries whose energy efficiency regulations h ave already been consolidated or are under consolidation, an important parameter to be verified is the pattern of relative consumption of electric energy of existing buildings according to ranges established for different typologies. This kind of verification is called the consumption or benchmarking. In Brazil, following the implementation in 2010 of the Technical Regulation of Quality for Energy Efficiency of Commercial, Services and Public Buildings - RTQ-C, it is considered that the next step to be taken in regulatory terms is to analyze the energy consumption pattern of buildings. This implies that part of this consumption is related to decisions taken during the design phase of the building. Thus, the main objective of this research was to establish, through actual data of consumption of 101 buildings, the influence of the design decisions on the energy consumption of the office towers in the city of Belo Horizonte MG. To achieve this aim, it was necessary to survey the electric energy consumption of this typology of buildings, as well as their constructive characteristics and the type of conditioning systems installed in them. For the classification of the buildings according to their consumption, a methodology was developed to isolate the annual consumption per square meter of the towers based on annual consumption data per square meter of the buildings as a whole and constructive data was collected through an Internet survey and on-site. With the annual consumption per square meter of the towers in hands, it was made a classification of this consumption by bands according to methodology proposed by EN 15217 - Energy Performance of Buildings Methods for expressing energy performance and for energy certification of buildings and afterwards a sensitivity analysis of the design variables (type of air conditioning, wall absorption, presence of shading, Solar heat gain coefficient, proportion between walls and windows WWR (Window-to-wall ratio), volume, coverage area, envelope area, shape and height factor) in relation to the proposed classification. It was verified that the type variables of air conditioning, wall absorption, Solar heat gain coefficient and WWR were correlated with the annual consumption of electric energy per square meter of the towers. The last step of the work was the development of a multivariate linear regression equation for the EUI forecast based on the variables previously mentioned.