Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

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.

Office Building’s Occupancy Prediction Using Extreme Learning Machine Model with Different Optimization Algorithms

“…The study uses data from open-office measurements in Vilnius. As it was already found by the authors in previously published study [15], best correlations in predicting occupancy behaviour are gained based on measured CO2 concentrations. Therefore, presented models are using as input data just PIR (occupancy sensors) and CO2 data.…”

“…Some monitoring studies showed that average occupancy in offices is just around 60 % [13], [14] and for multi-person offices it is higher and can reach ____________________________________________________________________________ 2021 / 25 527 90 % [14]. Meanwhile Bielskus et al [15] compared measured occupancy with the ones that are used in prediction and found that actual occupancy in an open-office is much lower compared to the DesignBuilder's default values and to the ones provided by EN 16798-1 [16]. These differences are accordingly 3.5 and 2.7 times [15].…”

“…Meanwhile Bielskus et al [15] compared measured occupancy with the ones that are used in prediction and found that actual occupancy in an open-office is much lower compared to the DesignBuilder's default values and to the ones provided by EN 16798-1 [16]. These differences are accordingly 3.5 and 2.7 times [15]. It can be summarized that realistic assumptions concerning the occupancy of the building may significantly decrease the EPG as well as contribute to increase of post-occupancy energy efficiency of the building through predictive-control.…”

“…These models can be categorized as deterministic, stochastic and machine learning. Authors in previously published paper [15] discussed different prediction models; also they are compared in a review paper [18].…”

“…As it was already concluded by the authors, that Extreme Learning Machine (ELM) method is promising and reliable in occupancy prediction [15]. The goal of the study is to apply two ELM models with different optimisation algorithms -Genetic (GA-ELM) and Simulated Annealing (SA-ELM) for occupancy prediction in an open-office building based on measured CO2 concentrations.…”