A multi-layer system for smart-buildings' functional and energy-efficiency awareness: Implementation on a real five-floors building

Broujeny, Roozbeh Sadeghian; Madani, Kurosh; Chebira, Abdennasser; Hurtard, Laurent

doi:10.1109/icawst.2017.8256529

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…The concerned heating spaces are located in a five-floor building of Senart Campus of the Université Paris Est Créteil (UPEC). The concerned building is automated, and it is the host of the Electrical Engineering and Industrial Informatics departments [21]. The concerned building is heated by a central heating system.…”

Section: The Framework Of the Living Spaces In Sbems Considering The mentioning

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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Section: The Framework Of the Living Spaces In Sbems Considering The mentioning

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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“…-Supervision layer (SL): It consists of a PC including TopKapi server supervision software (a supervisory control and data acquisition software), which acts as a supervision agent. It also includes a number of adequate interface agents (software units) concerning the control layer and storage memory [28]. It is relevant to note that, while nowadays micro-controllers are able to handle diverse computational skills, they may still be limited regarding computational needs relating to the context of the presented work.…”

Section: Implementation Of the Proposed Living-spaces' Dynamic Heatinmentioning

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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A Machine-Learning Based Approach for Data-Driven Identification of Heating Dynamics of Buildings' Living-Spaces

Broujeny

Madani

Chebira

et al. 2019

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applicat

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A multi-layer system for smart-buildings' functional and energy-efficiency awareness: Implementation on a real five-floors building

Cited by 3 publications

References 3 publications

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

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

A Machine-Learning Based Approach for Data-Driven Identification of Heating Dynamics of Buildings' Living-Spaces

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