Model Identification and Validation for a Heating System using MATLAB System Identification Toolbox

Rabbani, Muhammad Junaid; Hussain, Kashan; khan, Asim-ur-Rehman; Ali, Abdullah Marish

doi:10.1088/1757-899x/51/1/012022

Cited by 15 publications

(8 citation statements)

References 2 publications

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“…The experimental time-domain samples for Q p and C p are measured and compared with the response of the developed model. Based on the best fit ratio [21], the model presented good results for assessment and verification.…”

Section: System Identification This Section Aims To Developmentioning

confidence: 99%

Experimental investigation of energy consumption and model identification of reverse osmosis desalination system fed by hybrid renewable energy source under different operating conditions

Hossam-Eldin

Abed

Youssef

et al. 2019

IEEJ Transactions Elec Engng

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In this article, the specific energy consumption (SEC) and water production cost (Cw) of a reverse osmosis (RO) desalination plant are investigated experimentally under different working conditions. The plant is fed by a hybrid renewable energy source. The frequency of the high‐pressure pump motor is changed from 35 to 50 Hz using a variable frequency drive. In addition, the feed water temperature and the reject control valve are controlled. This article investigates the effects of SEC, Cw, permeate salt concentration, and plant productivity. The results show that both SEC and Cw are reduced by increasing the frequency up to 45 Hz before rising thereafter. Also, SEC and Cw decrease with increasing feed temperature and reduced reject valve opening. As a result, the optimum values of SEC and Cw are found at 45 Hz and 40 °C with 25% valve opening, representing 4.5 kWh/m3 and 0.55 $/m3, respectively. Then, a model is proposed for the RO plant using the Matlab identification toolbox. The model is validated by comparing the simulation results with the experimental data. Based on the best fit ratio, the model presents good results for verification, which can be used for a process control design. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: System Identification This Section Aims To Developmentioning

confidence: 99%

Experimental investigation of energy consumption and model identification of reverse osmosis desalination system fed by hybrid renewable energy source under different operating conditions

Hossam-Eldin

Abed

Youssef

et al. 2019

IEEJ Transactions Elec Engng

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“…In the work of [16], the identification of a heating system is done by investigation by means of an auto-regressive (ARX) model, auto-regressive and moving average (ARMAX) model, and Box-Jenkins (BJ) model. The target system includes a lamp and a metallic plate.…”

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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“…There is a stage called model estimation where its purpose is to get the minimum error function between actual and predicted output. There are various approaches that can be adopted in model estimation process to get the accurate model such as prediction error method, instrumental variable, least square and maximum likelihood [9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Varied Model Order in GA-optimised Parameter Estimation of Toothbrush Rig System

Ainul

Mahzan

Ahmad

et al. 2019

IJIE

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Parameter estimation is a vital part in constructing the best model of a dynamic system. This paper analyzed the performance of toothbrush rig parameter estimation using different model orders. Parameter estimation process of the system is performed through system identification. The approximate mathematical model that resembles the real system is obtained when the output is measured after loading the input signal. The application of real-coded genetic algorithm (RCGA) is proposed as optimization method in estimating the parameters of dynamic system. The best model is obtained by optimizing the objective function of mean squared errors. The performance is analyzed to get the approximate model of the real system using three different model orders with 10 times analysis for each model. A few criteria have been considered which are the optimization result of objective function, time execution and validation process. The estimated parameters are acceptable and possible to be used for controller development later on. Estimated parameter with model order 3 is chosen as the best model or the dynamic system as it has the highest performance compared to others.

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Model Identification and Validation for a Heating System using MATLAB System Identification Toolbox

Cited by 15 publications

References 2 publications

Experimental investigation of energy consumption and model identification of reverse osmosis desalination system fed by hybrid renewable energy source under different operating conditions

Experimental investigation of energy consumption and model identification of reverse osmosis desalination system fed by hybrid renewable energy source under different operating conditions

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

Evaluation of Varied Model Order in GA-optimised Parameter Estimation of Toothbrush Rig System

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