A recurrent neuro-fuzzy system and its application in inferential sensing

Jassar, S.; Liao, Zaiyi; Zhao, Lian

doi:10.1016/j.asoc.2010.11.011

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…The studied distribution network consists of two units of wind turbines located at buses 13 and 28. The turbine model is Nordex N100 [59]. Tables 3 and 4 indicate technical information of turbines.…”

Section: Case Studymentioning

confidence: 99%

Improving state estimation accuracy in active distribution networks by coordinating real‐time and pseudo‐measurements considering load uncertainty

Ajoudani

Shiekholeslami

Zakariazadeh

2022

IET Generation Trans & Dist

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Measurements at the level of active distribution networks can be classified into three major groups. Data collection and transmission involve the time interval of several seconds for the first category, several minutes to 24 h for the second category, and finally, several weeks to several months for the third category. Considering this three-level classification of the measurements, state estimation by these data should deal with three kinds of heterogeneous data, but it has undesirable effects on the state estimation process, such as lower accuracy of the results. Despite the existence of heterogeneous data and different time rates, this study presents a state estimation model for updating measurement devices in each of the categories, aiming at establishing coordination among the measured data and increasing the accuracy of the estimation. The opportunities and limitations of the proposed model are compared to those of the conventional models. To illustrate the effectiveness of the proposed method, two IEEE standard systems 34-bus and 123-bus, have been used. The experimental network includes various load patterns, as well as distributed generation sources whose uncertainty is considered in the proposed model. 1Various measurement devices are currently installed on distribution networks. They are compared in Table 1.

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Section: Case Studymentioning

confidence: 99%

Improving state estimation accuracy in active distribution networks by coordinating real‐time and pseudo‐measurements considering load uncertainty

Ajoudani

Shiekholeslami

Zakariazadeh

2022

IET Generation Trans & Dist

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“…This is an ON-OFF or PID controller. Jassar, Liao and Zhao have developed Recurrent Neuro-Fuzzy Inference System (RenFIS) based inferential model to estimate average air temperature in built environment [11]. The results show that RenFIS based inferential model is accurate and robust.…”

Section: Heatingmentioning

confidence: 99%

Inferential control - An advanced control strategy to save energy in residential heating systems

Jassar

Liao

Sharma

2012

2012 IEEE Electrical Power and Energy Conference

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The paper presents an advanced control strategy for residential heating systems to save energy and improve indoor environment. This strategy, named inferential control method, is based on Recurrent Neuro-Fuzzy Inference System. 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 allows for 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 setpoint, and results in energy savings and improved indoor environment.

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“…A combination of FL and NNs creates a system that has a high learning of thought, reasoning and defines such an improvement tool to determine uncertain behavior of dynamic and complex systems. This model has the advantage of specialized knowledge of fuzzy systems and training capability of the ANNs [5,6,19,20] .…”

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confidence: 99%

“…ANFIS system functions, including fast and accurate learning, is able to generalize well, explain and express fuzzy rules greatly and adapt the available data and expertise, and make them appropriate for a wide range of scientific and engineering applications [6] . Today, the Internet of Things (IoT) is an economic revolution which includes the main actors such as data volume and the immediacy.…”

mentioning

confidence: 99%

Outdoor Temperature Estimation Using ANFIS for Soft Sensors

2019

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In recent years, several studies using smart methods and soft computing in the field of HVAC systems has been provided. In this paper, we propose a framework which will strengthen the benefits of the fuzzy logic and neural fuzzy systems to estimate outdoor temperature. In this regard, ANFIS is used in effective combination of strategic information for estimating the outdoor temperature of the building. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Due to ANFIS accuracy in specialized predictions, it is an effective device to manage vulnerabilities of each experiential framework. The neural fuzzy system can concentrate on measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored. The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.

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A recurrent neuro-fuzzy system and its application in inferential sensing

Cited by 15 publications

References 22 publications

Improving state estimation accuracy in active distribution networks by coordinating real‐time and pseudo‐measurements considering load uncertainty

Improving state estimation accuracy in active distribution networks by coordinating real‐time and pseudo‐measurements considering load uncertainty

Inferential control - An advanced control strategy to save energy in residential heating systems

Outdoor Temperature Estimation Using ANFIS for Soft Sensors

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