Can occupant voting systems provide energy savings and improved occupant satisfaction in buildings?—a review

Khan, Donya Sheikh; Kolařík, Jakub

doi:10.1080/23744731.2021.1976017

Cited by 7 publications

(4 citation statements)

References 65 publications

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“…Literature [45,77] suggests that, compared to EMA, μEMA led to higher response rates, completion rates, and first prompt response rates while being perceived as less distracting. Occupants' states could be assessed by prompting questions on the screen (time-based) [45] or by designing a home screen application where subjects could give feedback whenever they want (event-based) [47].…”

Section: Micro Ecological Momentary Assessmentsmentioning

confidence: 99%

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Donkers

Vries

Yang

2024

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Occupant feedback enables building managers to improve occupants’ health, comfort, and satisfaction. However, acquiring continuous occupant feedback and integrating this feedback with other building information is challenging. This paper presents a scalable method to acquire continuous occupant feedback and directly integrate this with other building information. Semantic web technologies were applied to solve data interoperability issues. The Occupant Feedback Ontology was developed to describe feedback semantically. Next to this, a smartwatch app – Mintal – was developed to acquire continuous feedback on indoor environmental quality. The app gathers location, medical information, and answers on short micro surveys. Mintal applied the Occupant Feedback Ontology to directly integrate the feedback with linked building data. A case study was performed to evaluate this method. A semantic digital twin was created by integrating linked building data, sensor data, and occupant feedback. Results from SPARQL queries gave more insight into an occupant’s perceived comfort levels in the Open Flat. The case study shows how integrating feedback with building information allows for more occupant-centric decision support tools. The approach presented in this paper can be used in a wide range of use cases, both within and without the architecture, building, and construction domain.

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Section: Micro Ecological Momentary Assessmentsmentioning

confidence: 99%

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Donkers

Vries

Yang

2024

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“…Among energy-saving HVAC control methods, occupantfeedback control methods [9], in which inputs from occupants via smart devices (mainly smartphones), PCs, and thermostats are used for adjusting set-points of HVAC equipment, have been studied for more than 10 years [10]- [25]. Energy-saving occupant-feedback control methods aim at energy-saving while considering the comfort of occupants.…”

Section: Introductionmentioning

confidence: 99%

Energy-Saving Occupant-Feedback Control Method Under Preferred Air-Conditioner Settings of Occupants

Yano,

Sako

2024

IEEE Access

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This study evaluates an occupant-feedback control method under preferred air-conditioner settings of occupants. We performed an experiment where this method was actually used on inverter airconditioners in multi-occupancy office rooms in Japan in summer and winter for the case where the set-point temperatures of the air-conditioners could be changed freely by occupants. In this experiment, the method estimated acceptable set-point temperatures from data from the previous execution step and applied an energy-saving acceptable set-point temperature to the air-conditioners three times per day for a given period. To evaluate this method, which can be regarded as a nudge, we randomly allocated days where this method was and was not used. To determine the effectiveness of this method, we performed statistical analysis of the set-point temperatures and energy consumption analysis of the air-conditioners. The results showed excessive use of the air-conditioners during days without this method and mitigation of the excessive setpoint temperatures by this method. Possible reduction of energy consumption by this method was obtained, whereas some difficulties in the field study, such as uncontrollable usage of meeting rooms, prevented obtaining clear results for the energy-saving by this method. Our findings suggest that a slight change of setpoint temperatures may likely lead to energy-saving under preferred HVAC settings of occupants, indicating that statistical analysis of HVAC settings is helpful in confirming the effectiveness of the occupant-feedback control methods under actual HVAC usage.INDEX TERMS energy-saving, field study, heating ventilation and air-conditioning (HVAC), nudge, occupant-feedback, occupant-feedback HVAC control method.

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“…Such preferences can be identified by different procedures in the design or operation phase of the building. The most common are the questionnaires, feedback (required or volunteer) and the monitoring of actions and interactions between users and the systems' interface [7][8][9]. It is also possible to detect preferences by using immersive virtual environments [10][11][12]; however, some aspects of reality are not easy to simulate with realism [13].…”

Section: Introductionmentioning

confidence: 99%

Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users

Bilésimo,

Ghisi

2024

Sustainability

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Reducing energy consumption is vital to save natural resources and contribute to the sustainable development in any sector of society. In the building sector, there are many well-known energy efficiency strategies currently being applied. However, considering the advances in technology and in comfort studies, it is possible to see that the current building sector scenario demands new energy efficiency strategies. Such strategies need to be capable of identifying and assuring comfortable environments according to users’ perceptions. Machine learning techniques can be a useful alternative to identify users’ preferences and control lighting and heating, ventilation and air-conditioning systems in buildings. This paper shows a systematic literature review on the use of machine learning algorithms on preference identification and environmental adequacy according to users’ demands. Its contribution is to explore beyond the performance and configurations of the algorithms, addressing users’ preference aspects as well. The strategies found in the literature provided promising results. The most used approach was supervised learning because data can be treated as categories. In general, the control systems have shown good performance, and so have the algorithms. Users were mostly satisfied with environmental conditions. Situations of dissatisfaction were associated with the occupant’s willingness to use the system more than with the control system’s performance. Furthermore, it is also possible to ally user-centred control and energy savings but this relies on occupants’ characteristics and the control strategies used. We underline the importance of identifying whether the users are willing to deal with an automatic control system before making any decision, even if the operation of the system is based on their preferred environmental conditions.

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Can occupant voting systems provide energy savings and improved occupant satisfaction in buildings?—a review

Cited by 7 publications

References 65 publications

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Energy-Saving Occupant-Feedback Control Method Under Preferred Air-Conditioner Settings of Occupants

Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users

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