Abstract
Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.
Highlights
Despite increasing energy efficiency requirements for buildings across the EU, the full potential of energy efficiency is still unlocked
The premises of the open-space office building where the experimental research was conducted are occupied by the company that mainly focuses on renewable energy technologies
The experimentally defined occupancy schedules in an open-space office have shown that actual occupancy is less for about 3.5 times compared to the default DesignBuilder’s schedules and 2.7 times less compared to the ones given in EN 16798-1
Summary
Despite increasing energy efficiency requirements for buildings across the EU, the full potential of energy efficiency is still unlocked. Together with policy developments, building occupants’ behaviour plays an important role in achieving ambitious goals, as new consumption opportunities may arise because of energy efficiency gains, which may impair efforts to reduce overall energy consumption. The building sector remains one of the most energy intensive sectors, producing about one-third of total global greenhouse emissions in the EU [1]. During the design phase of the building, national regulations or standards like LEED or BREEAM usually dictate how much consideration must be given to the energy performance; ratings for energy performance certificates are produced to meet the requirements. There is significant evidence that buildings, when they are constructed, do not perform as was predicted during the design phase [2,3]. According to the platform CarbonBuzz [4], Energies 2020, 13, 4033; doi:10.3390/en13154033 www.mdpi.com/journal/energies
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