TY - JOUR AU - Qashou, Akram AU - Yousef, Sufian AU - Sanchez-Velazquez, Erika TI - Mining sensor data in a smart environment: a study of control algorithms and microgrid testbed for temporal forecasting and patterns of failure JF - INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT J2 - INT J SYST ASSUR ENGIN MANAG VL - 13 PY - 2022 IS - 5 SP - 2371 EP - 2390 PG - 20 SN - 0975-6809 DO - 10.1007/s13198-022-01649-7 UR - https://m2.mtmt.hu/api/publication/33318416 ID - 33318416 AB - The generation of active power in renewable energy is dependent on several factors. These variables are related to the areas of weather, physical structure, control, and load behavior. Estimating the future value of the active power to be generated is difficult due to their unpredictable character. However, because of the higher precision required of the estimation, this problem becomes more complex if we examine a short-term temporal prediction. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms to perform the Short-term estimate. The environment, the operation, and the generated (normal or faulty) signal are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been realized to conduct an experiment. In addition, the LSTM and the GRU are compared to see how well they perform in this system. The proposed method's end findings outperform the current state-of-the-art. LA - English DB - MTMT ER - TY - JOUR AU - Abu-Rub, Omar H. AU - Fard, Amin Y. AU - Umar, Muhammad Farooq AU - Hosseinzadehtaher, Mohsen AU - Shadmands, Mohammad B. TI - Towards Intelligent Power Electronics-Dominated Grid via Machine Learning Techniques JF - IEEE Power Electronics Magazine J2 - IEEE Power Electronics Magazine VL - 8 PY - 2021 IS - 1 SP - 28 EP - 38 PG - 11 SN - 2329-9207 DO - 10.1109/MPEL.2020.3047506 UR - https://m2.mtmt.hu/api/publication/32314336 ID - 32314336 LA - English DB - MTMT ER - TY - JOUR AU - Alqahtani, Ali AU - Ali, Mohammed AU - Xie, Xianghua AU - Jones, Mark W. TI - Deep Time-Series Clustering: A Review JF - ELECTRONICS (SWITZ) VL - 10 PY - 2021 IS - 23 SP - 3001 SN - 2079-9292 DO - 10.3390/electronics10233001 UR - https://m2.mtmt.hu/api/publication/32777928 ID - 32777928 LA - English DB - MTMT ER - TY - CHAP AU - Bhatnagar, Kaustubh AU - Sahoo, Subham AU - Iov, Florin AU - Blaabjerg, Frede TI - Physics Guided Data-Driven Characterization of Anomalies in Power Electronic Systems T2 - 2021 6th IEEE Workshop on the Electronic Grid (eGRID) SN - 9781665449793 PY - 2021 SP - 01 EP - 06 PG - 6 DO - 10.1109/eGRID52793.2021.9662148 UR - https://m2.mtmt.hu/api/publication/32777932 ID - 32777932 LA - English DB - MTMT ER - TY - JOUR AU - Haben, Stephen AU - Arora, Siddharth AU - Giasemidis, Georgios AU - Voss, Marcus AU - Vukadinović Greetham, Danica TI - Review of low voltage load forecasting: Methods, applications, and recommendations JF - APPLIED ENERGY J2 - APPL ENERG VL - 304 PY - 2021 SN - 0306-2619 DO - 10.1016/j.apenergy.2021.117798 UR - https://m2.mtmt.hu/api/publication/32777930 ID - 32777930 LA - English DB - MTMT ER - TY - JOUR AU - Li, Junxiang AU - Liu, Bo AU - Sun, Quan AU - Sarmadi, Kamran AU - Gao, Yan AU - Dang, Yazheng AU - Dong, Jingxin TI - Load shifting of a supplier-based demand response of multi-class subscribers in smart grid JF - INTERNATIONAL JOURNAL OF INDUSTRIAL AND SYSTEMS ENGINEERING J2 - INTERN J INDUSTR SYST ENGIN VL - 37 PY - 2021 IS - 4 SP - 506 SN - 1748-5037 DO - 10.1504/IJISE.2021.114054 UR - https://m2.mtmt.hu/api/publication/32777934 ID - 32777934 LA - English DB - MTMT ER - TY - JOUR AU - Smajla, Ivan AU - Sedlar, Daria Karasalihovic AU - Vulin, Domagoj AU - Jukic, Lucija TI - Influence of smart meters on the accuracy of methods for forecasting natural gas consumption JF - ENERGY REPORTS J2 - ENERGY REP VL - 7 PY - 2021 SP - 8287 EP - 8297 PG - 11 SN - 2352-4847 DO - 10.1016/j.egyr.2021.06.014 UR - https://m2.mtmt.hu/api/publication/33318417 ID - 33318417 AB - In 2019, natural gas accounted for 25.4% of gross inland consumption in the European Union (EU), making it one of the most important energy sources in the EU. The importance of natural gas, together with the ongoing liberalization of the gas market, has made the natural gas sector significantly commercially sensitive. To reduce the risk of financial losses, balance group managers often need to have an accurate forecast of natural gas consumption. An accurate forecast will ensure small deviations between actual gas consumption and reserved gas volumes and transmission system capacity resulting in less balancing energy required, which is sold at a higher price in the final balancing process.This paper researches the optimal number of smart meters and best fitted consumption data distribution in order to achieve satisfactory results in terms of the accuracy by using simple forecasting methods. Beside mentioned, this paper provides accuracy overview of various already available forecasting methods, as well as the selection of input parameters for forecasting short term natural gas consumption. Using the calculated linear temperature dependence together with the lognormal distribution, the consumption of natural gas was simulated for 12 different cases. The simulation showed that, if more than 10 000 smart meters were installed, deviation between average estimated natural gas consumption and the real data would be less than +/- 2.96 %. In case of 100 000 smart meters installed, deviation would be less than +/- 1.20 %, but the "large" partly temperature independent consumers must be disregarded. (C) 2021 The Authors. Published by Elsevier Ltd. LA - English DB - MTMT ER - TY - JOUR AU - Ibrahim, Muhammad Sohail AU - Dong, Wei AU - Yang, Qiang TI - Machine learning driven smart electric power systems: Current trends and new perspectives JF - APPLIED ENERGY J2 - APPL ENERG VL - 272 PY - 2020 PG - 19 SN - 0306-2619 DO - 10.1016/j.apenergy.2020.115237 UR - https://m2.mtmt.hu/api/publication/31484242 ID - 31484242 AB - The current power systems are undergoing a rapid transition towards their more active, flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in many domains, e.g., integration of various distributed renewable energy sources, cyberspace security, demand-side management, and decision-making of system planning and operation. The fulfillment of advanced functionalities in the smart grid firmly relies on the underlying information and communication infrastructure, and the efficient handling of a massive amount of data generated from various sources, e.g., smart meters, phasor measurement units, and various forms of sensors. In this paper, a comprehensive survey of over 200 recent publications is conducted to review the state-of-the-art practices and proposals of machine learning techniques and discuss the trend in a wide range of smart grid application domains. This study demonstrates the increasing interest and rapid expansion in the use of machine learning techniques to successfully address the technical challenges of the smart grid from various aspects. It is also revealed that some issues still remain open and worth further research efforts, such as the high-performance data processing and analysis for intelligent decision-making in large-scale complex multi-energy systems, lightweight machine learning-based solutions, and so forth. Moreover, the future perspectives of utilizing advanced computing and communication technologies, e.g., edge computing, ubiquitous internet of things and 5G wireless networks, in the smart grid are also highlighted. To the best of our knowledge, this is the first review of machine learning-driven solutions covering almost all the smart grid application domains. Machine learning will be one of the major drivers of future smart electric power systems, and this study can provide a preliminary foundation for further exploration and development of related knowledge and insights. LA - English DB - MTMT ER - TY - JOUR AU - Ali, Mohammed AU - Alqahtani, Ali AU - Jones, Mark W. AU - Xie, Xianghua TI - Clustering and Classification for Time Series Data in Visual Analytics: A Survey JF - IEEE ACCESS J2 - IEEE ACCESS VL - 7 PY - 2019 SP - 181314 EP - 181338 PG - 25 SN - 2169-3536 DO - 10.1109/ACCESS.2019.2958551 UR - https://m2.mtmt.hu/api/publication/31577947 ID - 31577947 AB - Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics. LA - English DB - MTMT ER - TY - JOUR AU - Ozturk, Suat AU - Ozturk, Feride TI - Forecasting Energy Consumption of Turkey by Arima Model JF - JOURNAL OF ASIAN SCIENTIFIC RESEARCH J2 - J ASIAN SCI RES VL - 8 PY - 2018 IS - 2 SP - 52 EP - 60 PG - 9 SN - 2226-5724 DO - 10.18488/journal.2.2018.82.52.60 UR - https://m2.mtmt.hu/api/publication/32777936 ID - 32777936 LA - English DB - MTMT ER - TY - CHAP AU - Tornai, Kálmán AU - Oláh, András AU - Drenyovszki, Rajmund AU - Kovács, Lóránt AU - Pintér, István AU - J, Levendovszky ED - IEEE, null TI - Recurrent neural network based user classification for smart grids T2 - 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) PB - IEEE CY - New York, New York SN - 9781538628904 PY - 2017 DO - 10.1109/ISGT.2017.8086043 UR - https://m2.mtmt.hu/api/publication/3314043 ID - 3314043 N1 - LA - English DB - MTMT ER - TY - CHAP AU - Sanayha, Manassakan AU - Vateekul, Peerapon TI - Fault Detection for Circulating Water Pump Using Time Series Forecasting and Outlier Detection T2 - International Conference on Knowledge and Smart Technology PB - IEEE SN - 9781467390774 PB - IEEE PY - 2017 SP - 193 EP - 198 PG - 6 DO - 10.1109/KST.2017.7886095 UR - https://m2.mtmt.hu/api/publication/26766575 ID - 26766575 LA - English DB - MTMT ER - TY - JOUR AU - Tornai, K AU - Oláh, A AU - Drenyovszki, R AU - Kovács, L AU - Pintér, I AU - Levendovszky, J TI - Deep learning based consumer classification for smart grid JF - LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES SOCIAL-INFORMATICS AND TELECOMMUNICATIONS ENGINEERING J2 - LECT NOTES INST COMPUT SCI SOC INF TELECOMMUN ENG VL - 203 PY - 2017 SP - 132 EP - 141 PG - 10 SN - 1867-8211 DO - 10.1007/978-3-319-61813-5_13 UR - https://m2.mtmt.hu/api/publication/26842095 ID - 26842095 LA - English DB - MTMT ER - TY - JOUR AU - Yiyan, LI AU - Dong, HAN AU - Zheng, YAN TI - Long-term system load forecasting based on data-driven linear clustering method JF - JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY J2 - J MODERN POWER SYSTEMS CLEAN ENERGY VL - x PY - 2017 SP - 1 EP - 11 PG - 11 SN - 2196-5625 DO - 10.1007/s40565-017-0288-x UR - https://m2.mtmt.hu/api/publication/26842134 ID - 26842134 LA - English DB - MTMT ER -