Influência do Comportamento do Usuário no Consumo de Energia em um Prédio de Ensino Público na Zona Bioclimática 2

Abstract

In view of the high energy consumption in public education buildings, it is necessary to understand the user’s behavior and role in the energy performance of buildings. This work seeks to identify the user’s influence on the energy performance of public education buildings. Three patterns of behavior were created: active, intermediate and passive. The active one aims the integration of natural and artificial lighting using passive solar shading and natural ventilation. The intermediate one does not seek the integration of natural and artificial lighting, but uses hybrid ventilation as a ventilation strategy. The passive user does not use any passive strategy. This profile uses artificial conditioning and keeps the blinds fully open. In addition, this user adjusts the setpoint of the cooling and heating temperature to values outside the norms. Aiming to investigate the impact of users’ behavior, this work developed an automated model of the building. This model uses the integration of natural and artificial lighting and ventilation, as well the control of an internal shading device was developed. The energy simulations were performed using EnergyPlus tool with the climate data of Pelotas-RS. The interaction between the artificial conditioning system and natural ventilation was controlled through the Energy Management System (EMS) and the EnergyPlus hybrid ventilation component. The influence of user behavior was analyzed through energy consumption for conditioning and artificial lighting during the occupation period. The results showed that the users of the building are located between the intermediate and the passive, with a tendency to the intermediate.The pattern of passive use of systems by users can be seen as a potential for intervention and education, with incentives for efficient energy use. Thus, there is an indicator of the potential for energy savings. Building automation represents a major investment, 259% or two and a half times. Compared with the investment in solar PV generation, it is possible to predict a lower cost-benefit ratio for automation than for PV energy generation.