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… Show more
“…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.…”
Section: Methods and Methodologymentioning
confidence: 92%
“…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].…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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].…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Increasing energy efficiency requirements lead to lower energy consumption in buildings, but at the same time occupants’ influence on the energy balance of the building during the use phase becomes more crucial. The randomness of the building’s occupancy often leads to the mismatch of the predicted and measured energy demand, also called Energy Performance Gap. Therefore, prediction of occupancy is important both in the design and use phases of the building. The goal of the study is to apply Extreme Learning Machine (ELM) models with different optimisation algorithms – Genetic (GA-ELM) and Simulated Annealing (SA–ELM) for occupancy prediction in an office building based on measured CO2 concentrations. Both models show similar and high accuracy of prediction: R2 – 0.73–0.74 and RMSE – 1.8–1.9 for the whole measured period. Influence of population size, number of neurons, and number of iterations on results accuracy was also analysed and recommendations are given. It was concluded that both methods are suitable for occupancy prediction, but because of different simulation times, SA-ELM is recommended for the Building Management Systems (BMS), where higher speed is required.
“…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.…”
Section: Methods and Methodologymentioning
confidence: 92%
“…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].…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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].…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Increasing energy efficiency requirements lead to lower energy consumption in buildings, but at the same time occupants’ influence on the energy balance of the building during the use phase becomes more crucial. The randomness of the building’s occupancy often leads to the mismatch of the predicted and measured energy demand, also called Energy Performance Gap. Therefore, prediction of occupancy is important both in the design and use phases of the building. The goal of the study is to apply Extreme Learning Machine (ELM) models with different optimisation algorithms – Genetic (GA-ELM) and Simulated Annealing (SA–ELM) for occupancy prediction in an office building based on measured CO2 concentrations. Both models show similar and high accuracy of prediction: R2 – 0.73–0.74 and RMSE – 1.8–1.9 for the whole measured period. Influence of population size, number of neurons, and number of iterations on results accuracy was also analysed and recommendations are given. It was concluded that both methods are suitable for occupancy prediction, but because of different simulation times, SA-ELM is recommended for the Building Management Systems (BMS), where higher speed is required.
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