Smart Control of Air Conditioning System Based on Number and Activity Level of Persons

Budiman, Fajar; Rivai, Muhammad; Raditya, Indra; Krisrenanto, Daniel; Amiroh, Irma Zahroul

doi:10.1109/isitia.2018.8711311

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Moreover, it provides the end user with more flexibility and extra information-there is no hard limit on the number of persons as opposed to classification-based approach. Using the presented approach, the approximate location of the persons in the field of view of the camera is known, which facilitates zone-based control [38], and paves the way to extensions such as inclusion of activity profiles in the climate control feedback loop [39]. All this is achieved using a simpler neural network architecture-the parameter count of the closest competing solution (in terms of functionality) is roughly triple.…”

Section: Discussionmentioning

confidence: 99%

Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings

Kraft

Aszkowski²,

Pieczyński³

et al. 2021

Energies

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Using passive infrared sensors is a well-established technique of presence monitoring. While it can significantly reduce energy consumption, more savings can be made when utilising more modern sensor solutions coupled with machine learning algorithms. This paper proposes an improved method of presence monitoring, which can accurately derive the number of people in the area supervised with a low-cost and low-energy thermal imaging sensor. The method utilises U-Net-like convolutional neural network architecture and has a low parameter count, and therefore can be used in embedded scenarios. Instead of providing simple, binary information, it learns to estimate the occupancy density function with the person count and approximate location, allowing the system to become considerably more flexible. The tests show that the method compares favourably to the state of the art solutions, achieving significantly better results.

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Section: Discussionmentioning

confidence: 99%

Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings

Kraft

Aszkowski²,

Pieczyński³

et al. 2021

Energies

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Indoor Thermal Comfort Controller Integrating Human Interaction in the Control-Loop as a Live Component

Segovia

Schaik

Vuković

2023

Springer Proceedings in Energy

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This research integrates the human component as a living part of the control loop, using preferences to optimise energy consumption. This paper presents an outline of a temperature controller, which is based on the theory of thermal comfort and uses fuzzy logic to optimise comfort and reduce energy consumption. The controller allows multiple-inputs, from more than one single user to set a temperature-setpoint. The control-logic was developed in MATLAB using the Simulink tool in the simulations, energy use is optimized, reducing energy consumption between 22 and 31%. The controller was tested in an office to improve the average thermal sensation of the participants between 14 and 17%. In future works increase the sample size and evaluate the non-energy impacts of the energy efficiency on thermal comfort.

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Classification of the Quality of Milk Using Spectrophotometer System Based on Raspberry Pi

Rivai

Budiman

Suwito

et al. 2019

2019 International Seminar on Intelligent Technology and Its Applications (ISITIA)

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Smart Control of Air Conditioning System Based on Number and Activity Level of Persons

Cited by 4 publications

References 2 publications

Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings

Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings

Indoor Thermal Comfort Controller Integrating Human Interaction in the Control-Loop as a Live Component

Classification of the Quality of Milk Using Spectrophotometer System Based on Raspberry Pi

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