Design of Fuzzy Logic Based Controller for Energy Efficient Operation in Smart Buildings

Omarov, Batyrkhan; Altayeva, Aigerim; Suleimenov, Zharas; Cho, Young Im; Omarov, Bauyrzhan

doi:10.1109/irc.2017.26

Cited by 24 publications

(15 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ECG signals has been extensively used in research on the diagnosis of cardiac illness [7][8][9][10][11]. PCG signals are made up of two primary tones, which are denoted by the notations "first sound" (S1) and "second sound" (S2) [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent times, the field of cardiology has seen a surge in the number of research projects that make use of data analysis. For the purpose of making an accurate diagnosis, the data obtained from PCG and ECG are examined [8][9][10][11]. The auscultation of the heart remains an essential diagnostic tool for determining the well-being of the cardiovascular system [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

Baikuvekov¹,

Tolep²,

Sultan³

et al. 2023

IJACSA

View full text Add to dashboard Cite

According to estimations made by World Health Organization, heart disease is the largest cause of mortality throughout the globe, and it is safe to assume that diagnosing heart diseases in their earliest stages is very essential. Diagnosis of cardiovascular disease may be carried out by detection of interference in cardiac signals, one of which is called phonocardiography, and it can be accomplished in a number of various ways. Using phonocardiogram (PCG) inputs and deep learning, the researchers aim to develop a classification system for different types of heart illness. The slicing and normalization of the signal served as the first step in the study's signal preprocessing, which was subsequently followed by a wavelet based transformation method that employs mother wavelet analytic morlet. The results of the decomposition are first shown with the use of a scalogram, afterwards, they are utilized as input for the deep CNN. In this investigation, the analyzed PCG signals were separated into categories, denoting normal and pathological heart sounds. The entire utilized data was divided into two categories as training and test data as 80% to 20%. The developed model demonstrates the degree of clinical diagnosis, sensitivity, specificity and AUC-ROC value.As a result, it has been determined that the proposed method was superior to the mother wavelet as well as other classifier approaches. Consequently, we were able to acquire an electronic stethoscope that has a diagnostic accuracy of more than 90% when it comes to identifying cardiac problems. To be more specific, the proposed deep CNN model has an accuracy of 93.25% in identifying aberrant heart sounds and 93.50% in identifying regular heartbeats. In addition, given the fact that an examination may be completed in only 15 seconds, speed is the primary advantage offered by the suggested stethoscope.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

Baikuvekov¹,

Tolep²,

Sultan³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Energy saving results derive from the difference between these two calculi. These FLC implementation not only reduce the spent energy through energy efficiency concept [15,16], "use less energy to obtain the same service", but also turns possible a contracted power reduction from 10.35kVA to 6.9kVA through the storage system integration which resulted on a 26.3€ saved every six months. These adopted measures allowed to change energy price tariff from the traditional one to time-of-use rate, accordingly to the Portuguese energy policy [17] resulting on 26.4€ saving in the considered time frame.…”

Section: Load Control 100%mentioning

confidence: 99%

Intelligent systems applied to buildings' management

Pereira

Barata

Viveiros

2020

2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)

View full text Add to dashboard Cite

Energy efficiency in buildings are an important domain regarding its impact in the global energy consumption. Several efficiency measures are currently available, using different methodologies. In this paper an approach using intelligent systems is implemented through a smart controller able to automatically and efficiently manage building's energy. This smart controller uses Fuzzy Logic Control (FLC) as a decision maker and is supported by Artificial Neural Networks (ANN) used to forecast the building outside temperature. In addition, and in order to increase overall energy efficiency, renewable energies associated with a storage system are considered. For the designed smart controller implementation, a thermal house model is simulated using a MATLAB toolbox. In this paper emphasis is given to the building´s Heating Ventilation and Air Conditioning (HVAC) control and a rough economic analysis is drawn, allowing to infer about the adopted efficiency measures impact.

show abstract

“…Another investigation based on fuzzy approaches was done by [15]. A heating and cooling system controller was introduced to bring a level of comfort for users and, in parallel, save energy.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Lack of information about the effects of dynamic factors in these works leads to simplistic solutions for facing control issues. In some works, like [14], the authors would like to decrease the effects of the dynamic parameters by a hierarchical fuzzy architecture, or in the work of [13,15], the practical operation of the error and error rate in a feedback loop helped reach the same goal. The authors of [9], with a fuzzy controller and considering dynamic parameters that have an influence on the control chain, faced that problem; however, in [18], the authors, by localization of the user, dealt with the control challenge, which was suffering from an effective algorithmic controller.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Design of Fuzzy Logic Based Controller for Energy Efficient Operation in Smart Buildings

Cited by 24 publications

References 5 publications

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

Intelligent systems applied to buildings' management

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

Contact Info

Product

Resources

About