@article{MTMT:33318416,
	title = {Mining sensor data in a smart environment: a study of control algorithms and microgrid testbed for temporal forecasting and patterns of failure},
	url = {https://m2.mtmt.hu/api/publication/33318416},
	author = {Qashou, Akram and Yousef, Sufian and Sanchez-Velazquez, Erika},
	doi = {10.1007/s13198-022-01649-7},
	journal-iso = {INT J SYST ASSUR ENGIN MANAG},
	journal = {INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT},
	volume = {13},
	unique-id = {33318416},
	issn = {0975-6809},
	abstract = {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.},
	keywords = {Renewable energy; Deep learning; smart home; Stochastic behavior; Short-term prediction},
	year = {2022},
	eissn = {0976-4348},
	pages = {2371-2390}
}

@article{MTMT:32314336,
	title = {Towards Intelligent Power Electronics-Dominated Grid via Machine Learning Techniques},
	url = {https://m2.mtmt.hu/api/publication/32314336},
	author = {Abu-Rub, Omar H. and Fard, Amin Y. and Umar, Muhammad Farooq and Hosseinzadehtaher, Mohsen and Shadmands, Mohammad B.},
	doi = {10.1109/MPEL.2020.3047506},
	journal-iso = {IEEE Power Electronics Magazine},
	journal = {IEEE Power Electronics Magazine},
	volume = {8},
	unique-id = {32314336},
	issn = {2329-9207},
	keywords = {Reliability; machine learning; power system reliability; Power electronics; performance evaluation; RESILIENCE; power system stability},
	year = {2021},
	pages = {28-38},
	orcid-numbers = {Umar, Muhammad Farooq/0000-0003-1232-0581}
}

@article{MTMT:32777928,
	title = {Deep Time-Series Clustering: A Review},
	url = {https://m2.mtmt.hu/api/publication/32777928},
	author = {Alqahtani, Ali and Ali, Mohammed and Xie, Xianghua and Jones, Mark W.},
	doi = {10.3390/electronics10233001},
	journal = {ELECTRONICS (SWITZ)},
	volume = {10},
	unique-id = {32777928},
	year = {2021},
	eissn = {2079-9292},
	pages = {3001},
	orcid-numbers = {Alqahtani, Ali/0000-0003-1052-2657; Ali, Mohammed/0000-0002-5908-4013; Xie, Xianghua/0000-0002-2701-8660; Jones, Mark W./0000-0001-8991-1190}
}

@inproceedings{MTMT:32777932,
	title = {Physics Guided Data-Driven Characterization of Anomalies in Power Electronic Systems},
	url = {https://m2.mtmt.hu/api/publication/32777932},
	author = {Bhatnagar, Kaustubh and Sahoo, Subham and Iov, Florin and Blaabjerg, Frede},
	booktitle = {2021 6th IEEE Workshop on the Electronic Grid (eGRID)},
	doi = {10.1109/eGRID52793.2021.9662148},
	unique-id = {32777932},
	year = {2021},
	pages = {01-06}
}

@article{MTMT:32777930,
	title = {Review of low voltage load forecasting: Methods, applications, and recommendations},
	url = {https://m2.mtmt.hu/api/publication/32777930},
	author = {Haben, Stephen and Arora, Siddharth and Giasemidis, Georgios and Voss, Marcus and Vukadinović Greetham, Danica},
	doi = {10.1016/j.apenergy.2021.117798},
	journal-iso = {APPL ENERG},
	journal = {APPLIED ENERGY},
	volume = {304},
	unique-id = {32777930},
	issn = {0306-2619},
	year = {2021},
	eissn = {1872-9118},
	orcid-numbers = {Haben, Stephen/0000-0001-6763-8314; Arora, Siddharth/0000-0001-6499-6941; Giasemidis, Georgios/0000-0002-6799-7285; Voss, Marcus/0000-0002-7811-3561}
}

@article{MTMT:32777934,
	title = {Load shifting of a supplier-based demand response of multi-class subscribers in smart grid},
	url = {https://m2.mtmt.hu/api/publication/32777934},
	author = {Li, Junxiang and Liu, Bo and Sun, Quan and Sarmadi, Kamran and Gao, Yan and Dang, Yazheng and Dong, Jingxin},
	doi = {10.1504/IJISE.2021.114054},
	journal-iso = {INTERN J INDUSTR SYST ENGIN},
	journal = {INTERNATIONAL JOURNAL OF INDUSTRIAL AND SYSTEMS ENGINEERING},
	volume = {37},
	unique-id = {32777934},
	issn = {1748-5037},
	year = {2021},
	eissn = {1748-5045},
	pages = {506}
}

@article{MTMT:33318417,
	title = {Influence of smart meters on the accuracy of methods for forecasting natural gas consumption},
	url = {https://m2.mtmt.hu/api/publication/33318417},
	author = {Smajla, Ivan and Sedlar, Daria Karasalihovic and Vulin, Domagoj and Jukic, Lucija},
	doi = {10.1016/j.egyr.2021.06.014},
	journal-iso = {ENERGY REP},
	journal = {ENERGY REPORTS},
	volume = {7},
	unique-id = {33318417},
	issn = {2352-4847},
	abstract = {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.},
	keywords = {simulation; Forecasting methods; smart metering; Lognormal distribution; Natural gas consumption; Input parameters},
	year = {2021},
	eissn = {2352-4847},
	pages = {8287-8297},
	orcid-numbers = {Smajla, Ivan/0000-0002-1009-6726; Vulin, Domagoj/0000-0002-8270-8971}
}

@article{MTMT:31484242,
	title = {Machine learning driven smart electric power systems: Current trends and new perspectives},
	url = {https://m2.mtmt.hu/api/publication/31484242},
	author = {Ibrahim, Muhammad Sohail and Dong, Wei and Yang, Qiang},
	doi = {10.1016/j.apenergy.2020.115237},
	journal-iso = {APPL ENERG},
	journal = {APPLIED ENERGY},
	volume = {272},
	unique-id = {31484242},
	issn = {0306-2619},
	abstract = {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.},
	keywords = {NEURAL NETWORKS; machine learning; Smart grid; Deep learning; Smart energy systems},
	year = {2020},
	eissn = {1872-9118}
}

@article{MTMT:31577947,
	title = {Clustering and Classification for Time Series Data in Visual Analytics: A Survey},
	url = {https://m2.mtmt.hu/api/publication/31577947},
	author = {Ali, Mohammed and Alqahtani, Ali and Jones, Mark W. and Xie, Xianghua},
	doi = {10.1109/ACCESS.2019.2958551},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {7},
	unique-id = {31577947},
	issn = {2169-3536},
	abstract = {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.},
	keywords = {CLASSIFICATION; VISUALIZATION; Clustering; Time series data; Visual analytics},
	year = {2019},
	eissn = {2169-3536},
	pages = {181314-181338},
	orcid-numbers = {Ali, Mohammed/0000-0002-5908-4013; Jones, Mark W./0000-0001-8991-1190; Xie, Xianghua/0000-0002-2701-8660}
}

@article{MTMT:32777936,
	title = {Forecasting Energy Consumption of Turkey by Arima Model},
	url = {https://m2.mtmt.hu/api/publication/32777936},
	author = {Ozturk, Suat and Ozturk, Feride},
	doi = {10.18488/journal.2.2018.82.52.60},
	journal-iso = {J ASIAN SCI RES},
	journal = {JOURNAL OF ASIAN SCIENTIFIC RESEARCH},
	volume = {8},
	unique-id = {32777936},
	issn = {2226-5724},
	year = {2018},
	eissn = {2223-1331},
	pages = {52-60}
}

@inproceedings{MTMT:3314043,
	title = {Recurrent neural network based user classification for smart grids},
	url = {https://m2.mtmt.hu/api/publication/3314043},
	author = {Tornai, Kálmán and Oláh, András and Drenyovszki, Rajmund and Kovács, Lóránt and Pintér, István and J, Levendovszky},
	booktitle = {2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
	doi = {10.1109/ISGT.2017.8086043},
	unique-id = {3314043},
	keywords = {PATTERN CLASSIFICATION; training; time series analysis; Power consumption; NEURAL NETS; hidden Markov models; Power transmission; Data models; Buildings; Recurrent neural networks; Smart grids; Smart power grids; learning (artificial intelligence); power engineering computing; recurrent neural nets; Consumer classification; unsupervised categorization; smart power transmission systems; recurrent neural network-based user classification; power consumption patterns; power consuming users; power consumers; nonlinear forecast techniques; measured power consumption data; consumption forecast based scheme; behavior forecast; load forecasting},
	year = {2017},
	orcid-numbers = {Tornai, Kálmán/0000-0003-1852-0816; Oláh, András/0009-0003-4796-8932; Drenyovszki, Rajmund/0000-0002-9462-2729}
}

@inproceedings{MTMT:26766575,
	title = {Fault Detection for Circulating Water Pump Using Time Series Forecasting and Outlier Detection},
	url = {https://m2.mtmt.hu/api/publication/26766575},
	author = {Sanayha, Manassakan and Vateekul, Peerapon},
	booktitle = {International Conference on Knowledge and Smart Technology},
	doi = {10.1109/KST.2017.7886095},
	publisher = {Institute of Electrical and Electronics Engineers},
	unique-id = {26766575},
	year = {2017},
	pages = {193-198}
}

@article{MTMT:26842095,
	title = {Deep learning based consumer classification for smart grid},
	url = {https://m2.mtmt.hu/api/publication/26842095},
	author = {Tornai, K and Oláh, A and Drenyovszki, R and Kovács, L and Pintér, I and Levendovszky, J},
	doi = {10.1007/978-3-319-61813-5_13},
	journal-iso = {LECT NOTES INST COMPUT SCI SOC INF TELECOMMUN ENG},
	journal = {LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES SOCIAL-INFORMATICS AND TELECOMMUNICATIONS ENGINEERING},
	volume = {203},
	unique-id = {26842095},
	issn = {1867-8211},
	year = {2017},
	eissn = {1867-822X},
	pages = {132-141}
}

@article{MTMT:26842134,
	title = {Long-term system load forecasting based on data-driven linear clustering method},
	url = {https://m2.mtmt.hu/api/publication/26842134},
	author = {Yiyan, LI and Dong, HAN and Zheng, YAN},
	doi = {10.1007/s40565-017-0288-x},
	journal-iso = {J MODERN POWER SYSTEMS CLEAN ENERGY},
	journal = {JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY},
	volume = {x},
	unique-id = {26842134},
	issn = {2196-5625},
	year = {2017},
	eissn = {2196-5420},
	pages = {1-11}
}