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- MakaleAçık Erişim2026Scopus SJRQ1Multi-Task NILM with Anomaly Detection Using a Hybrid CNN–BilSTM–Transformer Model
Gunay, Mihriban; Demir, Yakup; Zhilevski, Marin
Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics.
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İncele - MakaleAçık Erişim2026Scopus SJRQ2Hybrid Supervised Classification and Deep Embedding–Based Profiling Framework for Electricity Consumption Analysis
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Zhilevski, Marin; Mikhov, Mikho; Yordanov, Nikolay
This study proposes a hybrid deep learning framework that integrates supervised classification and unsupervised profiling for electricity consumption analysis. In the supervised phase, a one-dimensional Convolutional Neural Network combined with Long Short-Term Memory (1D CNN–LSTM) architecture is developed to classify daily load patterns. The performance of the proposed model is compared with traditional machine learning and deep learning approaches, including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), a standalone Long Short-Term Memory (LSTM) model, a Transformer-based model, and a standalone 1D CNN model. Experimental results on the Precon house dataset and the CU-BEMS dataset demonstrate that the proposed hybrid architecture outperforms the benchmark models, achieving classification accuracies of 87.59% and 86.40%, respectively. In the unsupervised phase, the trained CNN–LSTM encoder is utilized as a deep feature extractor. The resulting 32-dimensional latent embeddings are clustered using K-Means, Gaussian Mixture Model (GMM), Agglomerative, Spectral, and Ensemble methods. Clustering robustness is evaluated through bootstrap-based stability analysis using the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI). The results demonstrate stable and interpretable electricity consumption profiles, particularly in the residential dataset, where near-perfect clustering stability is observed for K-Means. The proposed framework provides both improved classification performance and robust consumption profiling based on deep embedding, offering a practical tool for energy management.
AtıfScopus1OpenAlex1İncele - Derleme MakaleAçık Erişim2025Deep learning: evolution, innovations, and applications in the last decade
Yıldırım, Özal; Günay, Mıhrıban; Demir, Yakup
Deep learning has become a popular method in the last ten years with its superior performance in many fields, especially in health. Although it is a sub-branch of machine learning, one of the most important reasons for researchers to use this method is that it automates the difficult processes of feature extraction stages in traditional machine learning methods. With the advancement of technology every year, it has become easier to create large data sets or to access large data sets that have been used before on the web. Researchers who want to work on large data sets use deep learning methods effectively because of their advantages instead of using traditional machine learning methods. The foundations of deep learning were laid with the deep belief networks method, which was first developed in 2006. Later, with the significant success of the Convolutional Neural Network (CNN) method developed in 2012 in image classification, deep learning methods have been used in many applications in other disciplines. The success of the deep reinforcement learning algorithm that defeated Alpha Go champion Lee Sedol in 2016, the remarkable success of the contentious generating networks in creating their own unique images, the success of the Siamese networks with the ability to learn from little data in signature verification and facial recognition systems, the success of the artificial intelligence chat bot ChatGPT, which was launched in the last months of 2022, attracted attention in a short time, and the ability of DALL-E, a similar language model, to create images from texts, shows that deep learning is in constant innovation and development. This study aims to give an idea to researchers who will work in this field in the future by talking about the basic concepts of deep learning and the innovative and popular approaches used.
- BildiriAçık Erişim2025Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU Models
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Benedicenti, L.; Liu, Z.
The rapid increase in the world population has increased electricity consumption and demand for electricity. In order for electricity distribution companies to determine electricity demand, household electricity consumption must be predicted. In this study, an open access dataset was used to predict individual household electricity consumption. For this purpose, deep learning-based LSTM, CNN-LSTM and CNN-GRU models were developed and a comparative analysis was performed. The performance of the models was evaluated using four evaluation metrics commonly used in prediction model performance evaluation. According to the results obtained, it was seen that the models used in the study were successful in predicting short-term electricity consumption. In particular, the CNNLSTM model was found to have a lower error rate compared to the other two models.
- Derleme MakaleYayıncıda açık erişim2020Scopus SJRQ1Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review
Murat, Fatma; Yildirim, Ozal; Talo, Muhammed; Baloglu, Ulas Baran; Demir, Yakup; Acharya, U. Rajendra
Deep learning models have become a popular mode to classify electrocardiogram (ECG) data. Investigators have used a variety of deep learning techniques for this application. Herein, a detailed examination of deep learning methods for ECG arrhythmia detection is provided. Approaches used by investigators are examined, and their contributions to the field are detailed. For this purpose, journal papers have been surveyed according to the methods used. In addition, various deep learning models and experimental studies are described and discussed. A five-class ECG dataset containing 100,022 beats was then utilized for further analysis of deep learning techniques. The constructed models were examined with this dataset, and results are presented. This study therefore provides information concerning deep learning approaches used for arrhythmia classification, and suggestions for further research in this area.
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İncele - Derleme MakaleYalnız künye2018Yeni Bir Çevrimiçi Elektrik Enerji Kalitesi İzleme Cihazı
Erişti, Hüseyin; Yıldırım, Özal; Demir, Yakup
Bu çalışmada, elektrik enerji kalitesi parametrelerini çevrimiçi izleyen yeni bir enerji kalite izleme cihazıgeliştirilmiştir. İzleme cihazı, bağlantı noktalarından üç faz akım ve gerilim bilgilerini gerçek zamanlı eldeederek güç kalitesi parametrelerini hesaplamaktadır. Ayrıca sahip olduğu haberleşme yapısı ile elde ettiği ölçümbilgilerini uzak bağlantı noktalarına anlık olarak aktarmaktadır. Cihaz yapısında, sinyal işleme alanında gittikçeyaygınlaşan Alanda Programlanabilir Kapı Dizileri (APKD) donanımı kullanılmıştır. Gömülü olarak hazırlananenerji izleme cihazı, kendi enerji akışlarını denetim altında tutmak isteyen kullanıcıların kolaylıklakullanabileceği etkin bir platform sunmaktadır.
- MakaleAçık Erişim2018Scopus SJRQ1FPGA-based online power quality monitoring system for electrical distribution network
Yildirim, Ozal; Eristi, Bekis; Eristi, Huseyin; Unal, Sencer; Erol, Yavuz; Demir, Yakup
In this paper, a new generation Internet-based Power Quality Monitoring System (IPQMS) that transmits real-time power quality (PQ) data over the internet has been developed. The monitoring system has the ability to measure PQ parameters in accordance with the related standards. This monitoring system includes many hardware and software designs and presents an efficient structure. PQ parameters are determined by signal processing algorithms that are applied to the current and voltage signals continuously obtained from the electrical power network. These signal processing algorithms are performed as embedded functions in the FPGA device. The PQ data obtained from the measurement points are transmitted to a server by UDP/IP communication protocol that is implemented in the FPGA device. The monitoring, reporting and permanently storing tasks are accomplished with the real-time automation software and web applications that running on the server computer. With its innovative software and hardware designs, the proposed monitoring approach presents a very useful monitoring structure that can be used in the PQ field.
AtıfScopus37WoS31İncele - MakaleAçık Erişim2018Scopus SJRQ2A new embedded power quality event classification system based on the wavelet transform
Eristi, Belkis; Yildirim, Ozal; Eristi, Huseyin; Demir, Yakup
In this paper, a real time embedded intelligent recognition system is proposed for diagnosing the power quality problems. The intelligent recognition system has a structure capable of classifying and detecting the power quality problems in real time. Hardware applications of the wavelet transform and decision tree classifier are realized inside the recognition system using with field programmable gate array (FPGA) device. These methods operating as embedded in the FPGA environment provide real-time diagnosis of power quality problems. A new approach of this recognition system is its capability to simultaneously detect a power quality event in the power systems and power quality disturbances occurring on each phase following the event. In this paper, 2 different recognition systems that online and offline are presented. The online recognition system operates in the fields with signal processing and classification structures embedded. In the offline system, the distinctive features of the event signals obtained from the online system are used and these signals are classified in the computer environment by means of the least square support vector machines. A prototype model of the power system is created in the laboratory environment in order to test the FPGA-based online intelligent recognition system, determine accuracy rates and evaluate its performance. Power quality event types are created in wide parameter ranges on this model. Obtained results from both recognition systems indicated the hardware and software designs of our embedded systems are quite effective, fast, and have high success performance.
AtıfScopus48WoS35OpenAlex50İncele - MakaleAçık Erişim2018Scopus SJRQ1An Agent-Based Pythagorean Fuzzy Approach for Demand Analysis with Incomplete Information
Baloglu, Ulas Baran; Demir, Yakup
Analyzing demand in environments with incomplete information is a challenging task. This paper proposes a novel agent-based Pythagorean fuzzy approach for analyzing this kind of demand. First, a Bayesian game is described with a large number of finite players, and this is followed by a Pythagorean fuzzy-based decision mechanism. Unlike the classical methods in the literature, the proposed method in this paper neither assumes nor forecasts the demand in a system. Instead, it tries to analyze the demand when there is limited availability of input data, or processing data are computationally expensive. The study ends with an application of the proposed system to an electricity grid. Electricity prices used as an incentive to construct an agent-based system that efficiently reduces the peak amounts in a smart grid by analyzing the demand. Test results provide evidence that the proposed approach is promising to design demand response systems.
AtıfScopus18WoS18OpenAlex19İncele - MakaleAçık Erişim2018Scopus SJRQ1Lightweight privacy-preserving data aggregation scheme for smart grid metering infrastructure protection
Baloglu, Ulas Baran; Demir, Yakup
The electric industry's planned shift to smart grid metering infrastructure raised several concerns especially about preserving the privacy. Various data perturbation and aggregation solutions are being developed to address these concerns. The drawback of these solutions is that a simple random noise scheme cannot protect privacy, and there is a need for more advanced perturbation techniques to increase hardware costs of smart metering devices. The proposed data aggregation scheme combines the power of perturbation techniques with crypto-systems in an efficient and lightweight way so that it becomes applicable for devices with limited hardware, such as smart meters. We investigated the privacy preserving capabilities of the proposed aggregation scheme with Holt-Winters and Seasonal Trend Decomposition using Loess prediction methods. The results indicate that the proposed scheme is resilient to both filtering and true value attacks.
AtıfScopus25WoS14OpenAlex25İncele - BildiriYalnız künye2017An Identity-Based Encryption Scheme with Performance Optimization for Privacy Preservation in Smart Grid Communication
Baloglu, Ulas Baran; Demir, Yakup; Paleologu, C; McClellan, S
Smart grids are expected to replace existing electrical grids with advanced mechanisms and intelligent entities, which have excessive data processing and communication tasks. It is important for the sensitive consumer data, such as meter readings, to be secured at these intelligent entities. Novel digital communication mechanisms should be developed to protect privacy. Existing smart grid architectures do not have sufficient and efficient security functionalities for data transmission. In this paper, we propose an Identity-Based Encryption scheme, which is more promising than the classical form regarding performance and security. The proposed scheme focuses on the data structure of meter readings and optimizes this structure to achieve a better privacy preservation. This novel structure further improves the performance of the scheme by reducing communication overhead caused by public key creation and distribution operations, as evaluations demonstrated.
AtıfWoS1İncele - BildiriAçık Erişim2017Economic Analysis of Hybrid Renewable Energy Systems with V2G Integration Considering Battery Life
Baloglu, Ulas Baran; Demir, Yakup; Caetano, N.S.; Faculty of Engineering University of Porto (FEUP), LEPABE, Rua Dr. Roberto Frias, Porto; Polytechnic Institute of Porto (IPP), CIETI, School of Engineering (ISEP), Rua Dr. Antonio Bernardino de Almeida 431, Porto; Felgueiras, M.C.; Forment, M.A.
Hybrid renewable energy systems considered as candidate solutions for environmental problems. Solar energy and wind energy are important components of these systems, but both of them cause fluctuations in the grid. Batteries of Electric Vehicles (EVs) can be connected to the grid to create an ancillary resource. These systems known as Vehicle to Grid (V2G) and they could be the most suitable solution to the fluctuation problems of renewable resources. In this paper, economic analysis of V2G is investigated from various perspectives. The findings of this study indicate that battery aging costs of a V2G system can be minimized with an intelligent energy management system, and these systems are both economically and environmentally better alternatives to fossil-fuelled generators.
AtıfScopus15WoS13OpenAlex16İncele - BildiriYalnız künye2017A fuzzy queueing based model for controlling power demand of electric vehicle charging
Baloglu, Ulas Baran; Demir, Yakup; Napoli, C.; University of Catania, Department of Mathematics and Informatics, Viale Andrea Doria 6, Catania; Wozniak, M.; Silesian University of Technology, Institute of Mathematics, Kaszubska 23, Gliwice; Damasevicius, R.; Kaunas University of Technology, Software Engineering Department, Studentu 50 415, Kaunas; Tramontana, E.
The rapid penetration of electric vehicles may lead to peak problems in a traditional grid so that some of the smart grid research is focused on charging strategies for electric vehicles. The charging problem is suitable for using a queue structure, and fuzzy queueing can be implemented for this purpose. This paper presents a fuzzy queueing based model, which can also control the power demand of electric vehicle charging. A charging model should guarantee that all charging requirements can be satisfied before vehicles leaving the charging stations. Simulation results exhibit that the proposed model decreases average waiting time of vehicles and also the proposed model utilize charging stations better than a traditional queueing model.
- BildiriAçık Erişim2017Face recognition based on convolutional neural network
Coskun, Musab; Ucar, Aysegul; Yildirim, Ozal; Demir, Yakup
Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.
AtıfScopus250OpenAlex295İncele - BildiriYalnız künye2016An Online Electric Power Quality Disturbance Detection System
Yildirim, Ozal; Eristi, Belkis; Eristi, Huseyin; Ünal, Sencer; Erol, Yavuz; Demir, Yakup
In this study, an FPGA based online monitoring system was developed for detection of power quality disturbances. The developed system has the capability to instantly detect commonly seen PQ disturbances such as voltage sag, voltage swell and interruption occurring on real-time three-phase voltage signals obtained from the electric power system. The data of the detected disturbance events are transferred to the visual software prepared in the computer environment by means of the UDP/IP communication module embedded in the FPGA. This PQ disturbance detection system was tested upon installation at three different measuring points across the campus of Tunceli University. It was seen from the test results examined that the detection system successfully identified the PQ disturbance events.
- BildiriYalnız künye2016A Real-Time Power Quality Disturbance Detection System Based On The Wavelet Transform
Eristi, Belkis; Yildirim, Ozal; Eristi, Huseyin; Demir, Yakup
Disturbances occurring on voltage signals following switching events or faults on power systems are defined as power quality problem. Power quality problems result in significant financial losses in the power systems in addition to faulty operation or breakdown of sensitive loads connected to the power system. Therefore, power quality problems should be swiftly detected and eliminated. This paper proposes a real-time embedded detection system for the purpose of identification of power quality disturbances. The detection system developed has been created using the wavelet transform in the Field Programmable Gate Array (FPGA) media. The general structure of FPGA-based detection system comprises two fundamental parts: hardware and software. Hardware structure comprises signal input card, FPGA and computer components. The software stage includes FPGA and graphical interface software. An experimental setup of the power system has been created in laboratory environment in order to test the FPGA-based detection system, determine accuracy rates and assess its success. Various power quality disturbances have been created over this model in wide parameter intervals. When the results obtained for the real-time power quality disturbance detection system developed under this study are examined, it has been seen that hardware and software designs are quite effective, fast and of high achievement performance.
- BildiriYalnız künye2015An FPGA Based Power Quality Monitoring System
Yildirim, Ozal; Eristi, Belkis; Eristi, Huseyin; Ünal, Sencer; Erol, Yavuz; Demir, Yakup
In this study, a new FPGA based monitoring system has been developed for monitoring the power quality. Designed system performs the calculations of power quality parameters on real-time voltages and currents datas that are obtained from the network. FPGA devices that have recently become more popular in digital signal processing field is used in the structure of monitoring system. Designed power quality monitoring system has been established on electrical distribution panels of Tunceli University Vocational Schools. When the obtained test results are evaluated, it has been observed that the proposed power quality monitoring system is successful.
AtıfWoS1İncele - MakaleAçık Erişim2015Scopus SJRQ2Automatic classification of harmonic data using k-means and least square support vector machine
Eristi, Huseyin; Tümen, Vedat; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using k-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a k-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the k-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.
AtıfWoS1OpenAlex3İncele - MakaleAçık Erişim2014Scopus SJRQ1Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine
Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, a new S-Transform and Extreme Learning Machine (ST-ELM)-based event recognition approach for the purpose of classifying power quality (PQ) event signals automatically has been proposed. In this approach, the distinctive features of the PQ event signals have been obtained with the S-Transform-based feature extraction. The feature vector obtained with feature extraction has been applied as input to the ELM classifier. Ten different classification procedures were determined within the framework of this study to assess the performance of the ELM classifier on PQ event data. Real PQ event data and synthetic PQ event data obtained from MATIAB/Simulink environment have been used in these procedures. Also, three different PQ event data sets, which are formed by adding noises of 20, 30 and 50 dB to the synthetic PQ event data respectively, have been used in order to assess the performance of the proposed approach on noisy conditions. According to the results of performance evaluations, the proposed ST-ELM-based PQ event recognition system has a very high performance of recognizing PQ event data. Besides, classification of noisy data showed that the proposed approach is robust at recognizing noisy data. The performance of the ST-ELM-based recognition system on PQ data shows that this approach has an effective recognition structure that can be used in real power systems. (C) 2014 Elsevier Ltd. All rights reserved.
AtıfWoS63OpenAlex93İncele - MakaleAçık Erişim2013Scopus SJRQ1Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines
Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy. (C) 2013 Elsevier Ltd. All rights reserved.
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