Design and simulation of an automatic room heater control system

Zungeru, Adamu Murtala; Mangwala, Mmoloki; Chuma, Joseph; Gaebolae, Baboloki; Basutli, Bokamoso

doi:10.1016/j.heliyon.2018.e00655

Cited by 21 publications

(16 citation statements)

References 8 publications

(11 reference statements)

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“…Where R is the surface reflectivity of the LDR, d is the depth from the silicon surface, α is the absorption coefficient, dependent on the material property (e.g., silicon or GaAs) q is the electron charge. Pin is the power of incident light; h is Planck's constant; and v is the frequency of incident light [8].…”

Section: Colour Sensormentioning

confidence: 99%

Automatic Bell Pepper Colour Detector and Sorting Machine

Seiphepi

Zungeru²,

Gaboitaolelwe³

et al. 2020

International Journal of Engineering Research and Technology

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Section: Colour Sensormentioning

confidence: 99%

Automatic Bell Pepper Colour Detector and Sorting Machine

Seiphepi

Zungeru²,

Gaboitaolelwe³

et al. 2020

International Journal of Engineering Research and Technology

Self Cite

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“…There are plenty of works based on model-free approaches to a building's heating. The authors of [7] proposed a heating controller for supporting the thermal satisfaction of the user. It was based on a thermostatic controller (operating on "On/Off") with a microcontroller.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Heating Controller Designing Based on Living Space Heating Dynamic’s Model Approach in a Smart Building

et al. 2021

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Most already advanced developed heating control systems remain either in a prototype state (because of their relatively complex implementation requirements) or require very specific technologies not implementable in most existing buildings. On the other hand, the above-mentioned analysis has also pointed out that most smart building energy management systems deploy quite very basic heating control strategies limited to quite simplistic predesigned use-case scenarios. In the present paper, we propose a heating control strategy taking advantage of the overall identification of the living space by taking advantage of the consideration of the living space users’ presence as additional thermal sources. To handle this, an adaptive controller for the operation of heating transmitters on the basis of soft computing techniques by taking into account the diverse range of occupants in the heating chain is introduced. The strategy of the controller is constructed on a basis of the modeling heating dynamics of living spaces by considering occupants as an additional heating source. The proposed approach for modeling the heating dynamics of living spaces is on the basis of time series prediction by a multilayer perceptron neural network, and the controlling strategy regarding the heating controller takes advantage of a Fuzzy Inference System with the Takagi-Sugeno model. The proposed approach has been implemented for facing the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil, taking into account the occupants of spaces in the control chain. The obtained results assessing the efficiency and adaptive functionality of the investigated fuzzy controller designed model-based approach are reported and discussed.

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“…A number of works address model-free approaches coping with buildings' heating. Related to conventional controllers, the authors of [7] introduced a control heating system for supporting the heating comfort of the user based on a very simple thermostatic controller (operating on an "on/off" strategy) with the help of a microcontroller. When the temperature is higher than the desired temperature, the fan will turn on, and when the temperature is lower than the desired temperature, the heater will turn on.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…The control systems designed in [7][8][9] operate without any pre-knowledge of the living spaces that they are supposed to heat. In other words, the proposed solutions are based exclusively on data provided by temperature sensors within the frame of specific edifices for which the model of heating-dynamics is available.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Data-Driven Living Spaces’ Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification

Broujeny

Madani

Chebira

et al. 2020

Sensors

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Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces’ heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space’s occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building’s living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron’s (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed.

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Design and simulation of an automatic room heater control system

Cited by 21 publications

References 8 publications

Automatic Bell Pepper Colour Detector and Sorting Machine

Automatic Bell Pepper Colour Detector and Sorting Machine

A Heating Controller Designing Based on Living Space Heating Dynamic’s Model Approach in a Smart Building

Data-Driven Living Spaces’ Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification

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