<abstract xmlns="http://www.w3.org/1999/xhtml">

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.
</abstract>

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.

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

Environmental and Climate Technologies

This work is licensed under the Creative Commons Attribution 4.0 International License.

Increasing energy efficiency requirements lead to lower energy consumption in buildings, but at the same time occupants’ influence on the energy balance of the...

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

Data publikacji: 20 wrz 2021

Zakres stron: 525 - 536

DOI: https://doi.org/10.2478/rtuect-2021-0038

Słowa kluczowe
CO (carbon dioxide), Genetic Algorithm (GA), office, Simulated Annealing (SA)

© 2021 Violeta Motuzienė et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 20 wrz 2021

Zakres stron: 525 - 536

DOI: https://doi.org/10.2478/rtuect-2021-0038

Słowa kluczoweCO (carbon dioxide), Genetic Algorithm (GA), office, Simulated Annealing (SA)

© 2021 Violeta Motuzienė et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
CO (carbon dioxide), Genetic Algorithm (GA), office, Simulated Annealing (SA)