scholarly journals Grey box modelling and advanced control scheme for building heating systems

2021 ◽  
Author(s):  
Surinder Jassar
Keyword(s):  
Air Temperature ◽  
Fuzzy Inference ◽  
Heating System ◽  
Hot Water ◽  
Training Data ◽  
Control Technology ◽  
Heating Systems ◽  
Inferential Control ◽  
Residential Heating ◽  
Neuro Fuzzy

This dissertation is aimed at generating new knowledge on Recurrent Neuro-Fuzzy Inference Systems (RenFIS) and to explore its application in building automation. Inferential sensing is an attractive approach for modeling the behavior of dynamic processes. Inferential sensor based control strategies are applied to optimize the control of residential heating systems and demonstrate significant energy saving and comfort improvement. Despite the rapidly decreasing cost and improving accuracy of most temperature sensors, it is normally impractical to use a lot of sensors to measure the average air temperature because the wiring and instrumentation can be very expensive to install and maintain. To design a reliable inferential sensor, of fundamental importance is to build a simple and robust dynamic model of the system to be controlled. This dissertation presents the development of an innovative algorithm that is suitable for the robust black-box model. The algorithm is derived from ANFIS (Adaptive Neuro-Fuzzy Inference System) and is referred to as RenFIS. Like all other modelling techniques, RenFIS performance is sensitive to the training data. In this study, RenFIS is used to model two different heating systems, hot water heating system and forced warm-air heating system. The training data is collected under different operational conditions. RenFIS gives better performance if trained with the data set representing overall qualities of the whole universe of the experimental data. The robustness analysis is conducted by introducing simulated noise to the training data. Results show that RenFIS is less sensitive than ANFIS to the quality of training data. The RenFIS based inferential sensor is then applied to design and inferential control algorithm that can improve the operation of residential heating systems. In current practice, the control of heating systems is based on measurement of the air temperature at one point within the building. The inferential control strategy developed through this study allows the control to be based on an estimate of the overall thermal performance. This is achieved through estimating the average room temperature using a RenFIS based inferential sensor and incorporating the estimate with conventional control technology. The performance of this control technology has been investigated through simulation study.

scholarly journals Grey box modelling and advanced control scheme for building heating systems

2021 ◽  
Author(s):  
Surinder Jassar
Keyword(s):  
Air Temperature ◽  
Fuzzy Inference ◽  
Heating System ◽  
Hot Water ◽  
Training Data ◽  
Control Technology ◽  
Heating Systems ◽  
Inferential Control ◽  
Residential Heating ◽  
Neuro Fuzzy

This dissertation is aimed at generating new knowledge on Recurrent Neuro-Fuzzy Inference Systems (RenFIS) and to explore its application in building automation. Inferential sensing is an attractive approach for modeling the behavior of dynamic processes. Inferential sensor based control strategies are applied to optimize the control of residential heating systems and demonstrate significant energy saving and comfort improvement. Despite the rapidly decreasing cost and improving accuracy of most temperature sensors, it is normally impractical to use a lot of sensors to measure the average air temperature because the wiring and instrumentation can be very expensive to install and maintain. To design a reliable inferential sensor, of fundamental importance is to build a simple and robust dynamic model of the system to be controlled. This dissertation presents the development of an innovative algorithm that is suitable for the robust black-box model. The algorithm is derived from ANFIS (Adaptive Neuro-Fuzzy Inference System) and is referred to as RenFIS. Like all other modelling techniques, RenFIS performance is sensitive to the training data. In this study, RenFIS is used to model two different heating systems, hot water heating system and forced warm-air heating system. The training data is collected under different operational conditions. RenFIS gives better performance if trained with the data set representing overall qualities of the whole universe of the experimental data. The robustness analysis is conducted by introducing simulated noise to the training data. Results show that RenFIS is less sensitive than ANFIS to the quality of training data. The RenFIS based inferential sensor is then applied to design and inferential control algorithm that can improve the operation of residential heating systems. In current practice, the control of heating systems is based on measurement of the air temperature at one point within the building. The inferential control strategy developed through this study allows the control to be based on an estimate of the overall thermal performance. This is achieved through estimating the average room temperature using a RenFIS based inferential sensor and incorporating the estimate with conventional control technology. The performance of this control technology has been investigated through simulation study.

Improve the Control of Residential Heating Systems

2011 ◽  
Vol 52-54 ◽  
pp. 1571-1576 ◽  
Author(s):  
Surinder Jassar ◽  
Zai Yi Liao
Keyword(s):  
Control Algorithm ◽  
Current Practice ◽  
Energy Savings ◽  
Heating Systems ◽  
Environment Quality ◽  
Inferential Control ◽  
Residential Heating ◽  
Neuro Fuzzy ◽  
Control Scheme ◽  
Indoor Environment Quality

A recurrent neuro-fuzzy based inferential sensor is applied to design an inferential control algorithm that can improve the operation of residential heating systems in which both energy efficiency and indoor environment quality are below expectation due to insufficient control. In current practice, the control of these heating systems is based on the measurement of air temperature at one point within the building. The inferential control strategy presented in this paper allows the control to be based on an estimate of the overall thermal performance, minimizing the chance of overheating (saving energy) and underheating (improving comfort) in the building. The performance of this control technology has been investigated through simulation study. The results show that the proposed control scheme can effectively maintain the temperature at set-point, and results in energy savings and improved thermal comfort.

scholarly journals Residential heating system selection using the generalized Choquet integral method with the perspective of energy

2018 ◽  
Vol 30 (1) ◽  
pp. 121-140 ◽  
Author(s):  
Yavuz Ozdemir ◽  
Sahika Ozdemir
Keyword(s):  
Integral Method ◽  
Fossil Fuels ◽  
Fuzzy Numbers ◽  
Choquet Integral ◽  
Heating System ◽  
Heating Systems ◽  
Energy Demands ◽  
Residential Heating ◽  
System Selection ◽  
Interdisciplinary Study

Using energy effectively is one of the most important issues and problems that countries should take up. As a parallel of increasing energy demands worldwide and still mostly using fossil fuels, energy saving issues have gained much importance in recent years for all areas of life. It is a fact that construction also plays an important role in the emergence of the energy and environmental problem that we see as the problem of our centenary. As buildings consume about 40% of the world's annual energy consumption globally, this study will focus on the evaluation of residential heating system alternatives using the generalized Choquet integral method with trapezoidal fuzzy numbers. The main contribution of this paper is to determine the interdependency among main criteria and subcriteria, the nonlinear relationship among them and the environmental uncertainties while prioritizing residential heating system alternatives using the generalized Choquet integral method with the experts’ view. To the authors’ knowledge, this will be the first interdisciplinary study that uses the generalized Choquet integral method for residential heating systems.

scholarly journals Application of Neural Networks for Control of District Heating \ Wykorzystanie Sieci Neuronowych Do Regulacji W Ciepłownictwie

2010 ◽  
Vol 56 (3) ◽  
pp. 219-238 ◽  
Author(s):  
W.J. Chmielnicki
Keyword(s):  
Neural Networks ◽  
Residential Buildings ◽  
District Heating ◽  
Heating System ◽  
Hot Water ◽  
System Control ◽  
Heating Systems ◽  
Quality Of Control ◽  
Building Maintenance ◽  
Non Linear

Abstract The annual usage of heat for the demand of heating systems in municipal sector has been estimated as about 650PJ. It is mostly addressed for the demand of central heating systems and hot water consumption. The mode of adopted solutions concerning regulation and control, as well as energy management system, essentially influence its consumption. In the case of residential buildings, the costs of energy constitute the greatest share related to the total cost of building maintenance. Providing buildings with modern digital systems for control and regulation of heating installations is a basic condition enabling their rational usage. In currently employed solutions, algorithms PI or PID are usually applied. However, due to the non-linear properties of heating control systems, they do not secure proper quality. The sequences are often unstable and major control deviations occur. The application of neural networks is an alternative solution to those presently employed. They are especially recommended for adaptive control of non-stationary systems. Such cases occur in heating objects since they demonstrate non-linear properties with a great range of variability of parameters; this especially refers to district heating equipped with flux-through heat exchangers. In this paper, a compile model of heating system control aided by neural networks is presented. The results of the investigation clearly prove the usefulness of such solutions, cause the quality of control is much better than that one applied in traditional systems. Presently, works on the implementation of the proposed solutions are under way.

scholarly journals Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System

2015 ◽  
Vol 55 ◽  
pp. 1-11
Author(s):  
Abdur Rosyid ◽  
Mohanad Alata ◽  
Mohamed El Madany
Keyword(s):  
Support System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Element Model ◽  
Training Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Rotor Bearing ◽  
Lqr Controller

This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.

Nonlinear System Identification of Smart Buildings

2017 ◽  
pp. 328-347
Author(s):  
Soroush Mohammadzadeh ◽  
Yeesock Kim
Keyword(s):  
System Identification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Computation Time ◽  
Principal Component ◽  
Training Data ◽  
Inference System ◽  
Smart Buildings ◽  
Neuro Fuzzy

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.

scholarly journals Proposal of a PCM Underfloor Heating System Using a Web Construction Method

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Sanghoon Baek ◽  
Jin Chul Park
Keyword(s):  
Energy Consumption ◽  
Thermal Storage ◽  
Heating System ◽  
Hot Water ◽  
Wire Mesh ◽  
Heating Systems ◽  
Temperature Range ◽  
Apartment Buildings ◽  
Storage Performance ◽  
Web Construction

Apartment buildings in Korea have adopted underfloor heating systems using web construction methods based on concrete and hot water systems. However, since such systems consume significant amounts of energy for heating owing to their low thermal storage performance, it is necessary to develop a new system that can minimize energy consumption by improving concrete thermal storage performance. This study proposes a phase-change material (PCM) underfloor heating system to reduce energy consumption in apartment buildings. An optimal design for a PCM underfloor heating system is proposed, and thermal storage performance of the proposed system is evaluated experimentally. The temperature range of the PCM for underfloor heating is also calculated considering the proposed design and comfortable heating conditions for domestic apartment buildings. Results indicate that a PCM underfloor heating system can be constructed in the following order: (1) a 210 mm concrete slab, (2) a 20 mm cushioning material, (3) 40 mm of mortar including a 10 mm PCM thermal storage container, and (4) 40 mm of finishing mortar including wire mesh and hot water pipes. The temperature range of the PCM used for underfloor heating in domestic apartment buildings is 32–45°C. Experimental tests reveal that thermal storage performance of underfloor heating systems that apply 35, 37, 41, and 44°C as representative PCM temperatures is superior to existing systems.

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