scholarly journals Physical-Rules-Based Adaptive Neuro-Fuzzy Inferential Sensor Model for Predicting the Indoor Temperature in Heating Systems

2012 ◽  
Vol 8 (6) ◽  
pp. 517826
Author(s):  
Liang Huang ◽  
Zaiyi Liao ◽  
Lian Zhao
Keyword(s):  
Indoor Temperature ◽  
Heating Systems ◽  
Sensor Model ◽  
Neuro Fuzzy

Physical Rules Based Adaptive Neuro-Fuzzy Inferential Sensor Model Design and Analysis in Predicting the Indoor Temperature in Heating System

2012 ◽  
Vol 516-517 ◽  
pp. 370-379 ◽  
Author(s):  
Liang Huang ◽  
Zai Yi Liao ◽  
Hua Ge ◽  
Lian Zhao
Keyword(s):  
Energy Efficiency ◽  
Prediction Model ◽  
Performance Measures ◽  
Heating System ◽  
Model Design ◽  
Indoor Temperature ◽  
Sensor Model ◽  
Neuro Fuzzy ◽  
Robust Prediction ◽  
Indoor Comfort

The previous research on adaptive neuro-fuzzy inferential systems (ANFIS) presented an approach to estimating the average indoor temperature in the building environment. However, the restriction on robustness limited the energy efficiency and indoor comfort ratio. An accurate and robust prediction model is proposed in this paper. Comparing to the previous unphysical rules based ANFIS prediction model, the improvement of the physical rules based ANFIS prediction model will be presented and the reason of better performance of this new model will be discussed. Three performance measures are using in evaluating the proposed prediction model.

scholarly journals Physical-Rule Based Adaptive Neuro-Fuzzy Inferential Sensor For Optimal Control Of Space Heating Systems

2021 ◽  
Author(s):  
Liang Huang
Keyword(s):  
Optimal Control ◽  
Prediction Model ◽  
High Energy ◽  
Space Heating ◽  
Indoor Temperature ◽  
Rule Based ◽  
Heating Systems ◽  
Neuro Fuzzy ◽  
Physical Sensors ◽  
Robust Prediction

The previous research on adaptive neuro-fuzzy inferential sensor (ANFIS) presented an approach to estimate the average indoor temperature and proposed a new method to measure process variables which are impossible to measure directly by using physical sensors in buildings. To achieve high energy efficiency in heating systems, an accurate and robust prediction model is essential. This thesis aims to improve the conventional ANFIS indoor temperature estimator and look for an optimal control of space heating systems. A physical-rule based ANFIS prediction model is proposed. The differences between this physical-rule based ANFIS prediction model and the conventional ANFIS prediction model are presented and analyzed. Three performance measures (RMSE, RMS, and R

scholarly journals Physical-Rule Based Adaptive Neuro-Fuzzy Inferential Sensor For Optimal Control Of Space Heating Systems

2021 ◽  
Author(s):  
Liang Huang
Keyword(s):  
Optimal Control ◽  
Prediction Model ◽  
High Energy ◽  
Space Heating ◽  
Indoor Temperature ◽  
Rule Based ◽  
Heating Systems ◽  
Neuro Fuzzy ◽  
Physical Sensors ◽  
Robust Prediction

The previous research on adaptive neuro-fuzzy inferential sensor (ANFIS) presented an approach to estimate the average indoor temperature and proposed a new method to measure process variables which are impossible to measure directly by using physical sensors in buildings. To achieve high energy efficiency in heating systems, an accurate and robust prediction model is essential. This thesis aims to improve the conventional ANFIS indoor temperature estimator and look for an optimal control of space heating systems. A physical-rule based ANFIS prediction model is proposed. The differences between this physical-rule based ANFIS prediction model and the conventional ANFIS prediction model are presented and analyzed. Three performance measures (RMSE, RMS, and R

A neuro–fuzzy model for prediction of the indoor temperature in typical Australian residential buildings

2009 ◽  
Vol 41 (7) ◽  
pp. 703-710 ◽  
Author(s):  
Haitham Alasha’ary ◽  
Behdad Moghtaderi ◽  
Adrian Page ◽  
Heber Sugo
Keyword(s):  
Residential Buildings ◽  
Fuzzy Model ◽  
Indoor Temperature ◽  
Neuro Fuzzy

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.

Physical-Rule Based Adaptive Neuro-Fuzzy Inferential Sensor vs. GA-BP Based Prediction Model in Indoor Temperature Predicting

2012 ◽  
Vol 594-597 ◽  
pp. 2179-2185
Author(s):  
Liang Huang ◽  
Zai Yi Liao
Keyword(s):  
Prediction Models ◽  
Back Propagation ◽  
District Heating ◽  
Heating System ◽  
Good Prediction ◽  
Indoor Temperature ◽  
District Heating System ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
Indoor Comfort

The previous research on temperature prediction presented different approaches which are physical-rule based adaptive neuro-fuzzy inferential sensor (ANFIS) model and GA-BP (genetic algorithm back propagation) based model to estimate the average indoor temperature in the building environment. Their good prediction performances improved energy efficiency of district heating system and indoor comfort ratio. However, either of these two models has its drawback in a certain condition. In this paper, the two prediction models are reviewed and evaluated by three performance measures (RMSE, RMS, and R2). Their limitations are discussed and potential solution is proposed.

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.

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