Energy consumption prediction in an office building by examining occupancy rates and weather parameters using the moving average method and artificial neural network

Abstract

Occupancy rate refers to the level of usage and presence of individuals within a building or a specific space. This factor can have a significant impact on building energy consumption. When the occupancy rate in a building is high, naturally, energy consumption also increases. This correlation might be due to the increased use of lighting, heating, and cooling, higher numbers of electrical and electronic devices, and similar factors associated with the presence of people in the building. One of the modern methods in the energy field involves empirically utilizing occupancy monitoring tools in buildings and analyzing the relationship between such utilization and building energy consumption through artificial neural network tools. In this research, a camera sensitive to entry and exit was installed at the entrance of an office building in Tehran, Iran. By doing so, the rate of entry and exit was accurately monitored. In the next stage, by investigating the impact of this entry and exit rate on the building's energy consumption, the energy consumption amount was predicted using an artificial neural network and a statistical method (moving average). The results indicate errors of 9.8 and 4.5 for the respective methods, highlighting that the artificial neural network yields the most accurate outcomes. Moreover, the study's findings suggest a direct correlation: as occupancy rates increase, the predicted energy consumption values also rise.