Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Jonas Bielskus, Violeta Motuzienė, Tatjana Vilutienė and Audrius Indriulionis
Additional contact information
Jonas Bielskus: Department of Building Energetics, Faculty of Environmental Engineering, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
Violeta Motuzienė: Department of Building Energetics, Faculty of Environmental Engineering, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
Tatjana Vilutienė: Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
Audrius Indriulionis: Department of Business Technologies and Entrepreneurship, Faculty of Business Management, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania

Energies, 2020, vol. 13, issue 15, 1-20

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 R 2 = 0.72. The best correlation was found with indoor CO 2 concentration—R 2 = 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.

Keywords: open-space office; occupancy prediction; energy-performance gap; online sequential extreme learning machine; DE-OSELM method; differential evolution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/15/4033/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/15/4033/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:15:p:4033-:d:394387

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().