Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

• Published in: Energies (Basel) Vol. 13; no. 15; p. 4033
• Main Authors: Bielskus, Jonas, Motuzienė, Violeta, Vilutienė, Tatjana, Indriulionis, Audrius
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2020
• Subjects: Algorithms; Architecture; Buildings; Carbon dioxide; Correlation analysis; Correlation coefficient; Correlation coefficients; DE-OSELM method; differential evolution; Energy consumption; Energy efficiency; Energy management; energy-performance gap; Evolutionary computation; Green buildings; Humidity; Indoor air pollution; Indoor air quality; Internet; Learning algorithms; Occupancy; occupancy prediction; online sequential extreme learning machine; open-space office; Physical properties; Prediction models; Sensors; Simulation; Sustainable design
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13154033

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.