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A platform for research: civil engineering, architecture and urbanism
A simulation-based framework to optimize occupant-centric controls given stochastic occupant behaviour
https://orcid.org/0000-0002-6827-269X
Abstract Occupant-centric control (OCC) strategies represent a novel approach for indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve occupant comfort and energy efficiency by learning and predicting occupant behaviour (OB), then optimizing building operations accordingly. Previous studies estimate that OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subject to several factors, including uncertainty due to OB, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings prior to implementation are critical to ensure optimal performance. Furthermore, identifying building design alternatives that optimize such performance given different occupant preferences is an important step that faces many logistical constraints during field implementations. This paper presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic OB with OCCs that learn preferences. The genetic algorithm for multi-objective optimization is then used to identify the configurational settings and design parameters that minimize energy consumption and maximize occupant comfort under various occupant scenarios. To demonstrate the proposed framework, three OCCs were implemented in the building simulation program, EnergyPlus, and executed through Python packages to optimize OCC configurations and design parameters. Results revealed significant improvement of OCC performance when they were customized with the identified optimal configurational settings for different occupant scenarios. It was found that these optimal points could reduce energy consumption by up to 33% while improving occupant comfort by up to 28%, relative to a baseline scenario with non-optimized OCC implementation. The proposed framework aims to improve OCC performance in actual buildings and avoid discomfort issues that may arise during its initial implementation phases.
Highlights A framework to optimize the performance of Occupant-centric controls was presented. A sensitivity analysis method based on the finite differences method was used. A two-step multi-objective optimization method was developed. A case study was conducted for an office building model as a proof-of-concept. The results showed ∼30% increase in energy-saving and occupant comfort.
A simulation-based framework to optimize occupant-centric controls given stochastic occupant behaviour
https://orcid.org/0000-0002-6827-269X
Abstract Occupant-centric control (OCC) strategies represent a novel approach for indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve occupant comfort and energy efficiency by learning and predicting occupant behaviour (OB), then optimizing building operations accordingly. Previous studies estimate that OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subject to several factors, including uncertainty due to OB, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings prior to implementation are critical to ensure optimal performance. Furthermore, identifying building design alternatives that optimize such performance given different occupant preferences is an important step that faces many logistical constraints during field implementations. This paper presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic OB with OCCs that learn preferences. The genetic algorithm for multi-objective optimization is then used to identify the configurational settings and design parameters that minimize energy consumption and maximize occupant comfort under various occupant scenarios. To demonstrate the proposed framework, three OCCs were implemented in the building simulation program, EnergyPlus, and executed through Python packages to optimize OCC configurations and design parameters. Results revealed significant improvement of OCC performance when they were customized with the identified optimal configurational settings for different occupant scenarios. It was found that these optimal points could reduce energy consumption by up to 33% while improving occupant comfort by up to 28%, relative to a baseline scenario with non-optimized OCC implementation. The proposed framework aims to improve OCC performance in actual buildings and avoid discomfort issues that may arise during its initial implementation phases.
Highlights A framework to optimize the performance of Occupant-centric controls was presented. A sensitivity analysis method based on the finite differences method was used. A two-step multi-objective optimization method was developed. A case study was conducted for an office building model as a proof-of-concept. The results showed ∼30% increase in energy-saving and occupant comfort.
A simulation-based framework to optimize occupant-centric controls given stochastic occupant behaviour
https://orcid.org/0000-0002-6827-269X
Abstract Occupant-centric control (OCC) strategies represent a novel approach for indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve occupant comfort and energy efficiency by learning and predicting occupant behaviour (OB), then optimizing building operations accordingly. Previous studies estimate that OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subject to several factors, including uncertainty due to OB, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings prior to implementation are critical to ensure optimal performance. Furthermore, identifying building design alternatives that optimize such performance given different occupant preferences is an important step that faces many logistical constraints during field implementations. This paper presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic OB with OCCs that learn preferences. The genetic algorithm for multi-objective optimization is then used to identify the configurational settings and design parameters that minimize energy consumption and maximize occupant comfort under various occupant scenarios. To demonstrate the proposed framework, three OCCs were implemented in the building simulation program, EnergyPlus, and executed through Python packages to optimize OCC configurations and design parameters. Results revealed significant improvement of OCC performance when they were customized with the identified optimal configurational settings for different occupant scenarios. It was found that these optimal points could reduce energy consumption by up to 33% while improving occupant comfort by up to 28%, relative to a baseline scenario with non-optimized OCC implementation. The proposed framework aims to improve OCC performance in actual buildings and avoid discomfort issues that may arise during its initial implementation phases.
Highlights A framework to optimize the performance of Occupant-centric controls was presented. A sensitivity analysis method based on the finite differences method was used. A two-step multi-objective optimization method was developed. A case study was conducted for an office building model as a proof-of-concept. The results showed ∼30% increase in energy-saving and occupant comfort.
A simulation-based framework to optimize occupant-centric controls given stochastic occupant behaviour
Khorasani Zadeh, Zeinab (author) / PhD Ouf, Mohamed M. (author)
Building and Environment ; 219
2022-04-22
Article (Journal)
Electronic Resource
English
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