مروری جامع بر روش های داده محور در شبکه های هوشمند برق

محورهای موضوعی : انرژی های تجدیدپذیر

خالق بهروز دهکردی ¹ (دانشکده مهندسی کامپیوتر- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
هما موحدنژاد ² (دانشکده مهندسی کامپیوتر- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
مهدی شریفی ³ (مرکز تحقیقات کلان داده- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)

تاریخ دریافت : 1400/09/19 تاریخ پذیرش : 1401/02/13 تاریخ انتشار : 1401/02/13

کلید واژه: یادگیری عمیق, شبکه هوشمند برق, یادگیری ماشین, روش‌های داده‌محور, کلان‌داده,

چکیده مقاله :

امروزه شبکه برق به طور چشم گیری در حال تبدیل شدن به شبکه هوشمند (SG)، به عنوان یک چشم انداز امیدوارکننده برای برخورداری از قابلیت اطمینان بالا و مدیریت کارآمد انرژی است. این انتقال به طور پیوسته در حال تغییر است و نیازمند روش های پیشرفته برای پردازش کلان داده های تولید شده از بخش های مختلف است. روش های هوش مصنوعی می توانند از طریق استخراج اطلاعات ارزشمند که توسط دستگاه های اندازه گیری و سنسور های موجود در شبکه تولید می شوند خدمات مبتنی بر داده را ارائه نمایند. به این منظور روش های یادگیری ماشین، یادگیری عمیق، یادگیری تقویتی و یادگیری عمیق تقویتی می توانند به کار گرفته شوند. این روش ها می توانند حجم زیادی از داده های جمع آوری شده را پردازش نموده و راه حل مناسبی برای مشکلات پیچیده صنعت برق ارائه نمایند. از این رو در این مقاله آخرین رویکرد های مبتنی بر هوش مصنوعی مورد استفاده در شبکه هوشمند برق برای کاربرد ها و منابع داده به طور جامع بررسی شده است. همچنین نقش کلان داده در شبکه هوشمند برق و ویژگی های آن از جمله چرخه حیات کلان داده و رویکردهای موثر آن مانند پیشگویی، تعمیرات قابل پیش بینی و تشخیص خطا در صنعت برق بیان می شود.

چکیده انگلیسی:

As a promising vision toward obtaining high reliability and better energy management, nowadays power grid is transferring to the smart grid (SG). This process is changing continuously and needs advanced methods to process big data produced by different segments. Artificial intelligence methods can offer data-driven services by extracting valuable information which is produced by meter devices and sensors in smart grids. To this end, machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) can be applied. These methods are able to process huge amounts of data and propose an appropriate solution to solve power industry complex problems. In this paper, the state-of-the-art approaches based on artificial intelligence used by smart power grids for applications and data sources are investigated. Also, the role of big data in smart power grids, and its features such life cycle, and efficient services such as forecast, predictive maintenance, and fault detection are discussed.

منابع و مأخذ:

[1] K. Zhou, C. Fu, S. Yang, "Big data driven smart energy management: From big data to big insights", Renewable and Sustainable Energy Reviews, vol. 56, pp. 215-225, April 2016 (doi: 10.1016/j.rser.2015.11.050).

[2] S. Zhao, F. Blaabjerg, H. Wang, "An overview of artificial intelligence applications for power electronics", IEEE Transactions. on Power Electronics, vol. 36, no. 4, pp. 4633-4658, April 2021 (doi: 10.1109/TPEL.2­020.30­249­14).

[3] D. Syed, A. Zainab, A. Ghrayeb, S.S. Refaat, H. Abu-Rub, O. Bouhali, "Smart grid big data analytics: Survey of technologies, techniques, and applications", IEEE Access, vol. 9, pp. 59564-59585, 2021 (doi: 10.1109/ACCESS.2020.3041178).

[4] M. Mishra, J. Nayak, B. Naik, A. Abraham, "Deep learning in electrical utility industry: A comprehensive review of a decade of research", Engineering Applications of Artificial Intelligence, vol. 96, Article Number: 104000, Nov. 2020 (doi: 10.1016/j.engappai.2020.104000).

[5] R. Arghandeh, Y.Z. Eds, "Big data application in power systems", Elsevier, 2018 (ISBN: 978-0-12-811968-6).

[6] V. Chandola, A. Banerjee, V. Kumar, "Anomaly detection: A survey", ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, July 2009 (doi: 10.1145/1541880.1541882).

[7] V.J. Hodge, J. Austin, "A survey of outlier detection methodologies", Artificial Intelligence Review, vol. 22, no. 2, pp. 85-126, 2004 (doi: 10.1007/s10462-004-4304-y).

[8] L. Erhan, M. Ndubuaku, M. Di Mauro, W. Song, M. Chen, G. Fortino, O. Bagdasar, A. Liotta, "Smart anomaly detection in sensor systems: A multi-perspective review", Information Fusion, vol. 67, pp. 64-79, Mar 2021 (doi: 10.1016/j.inffus.2020.10.001).

[9] N. Mohamudally, M. Peermamode-Mohaboob, "Building an anomaly detection engine (ADE) for IoT smart applications", Procedia Computer Science, vol. 134, pp. 10-17, 2018 (doi: 10.1016/j.procs.2018.07.138).

[10] M. Munir, S. A. Siddiqui, M.A. Chattha, A. Dengel, S. Ahmed, "FuseAD: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models", Sensors, vol. 19, no. 11, 2019 (doi: 10.3390/s19112451).

[11] D. Brauckhoff, K. Salamatian, M. May, "A signal processing view on packet sampling and anomaly detection", Proceedings of the IEEE/INFOCOM, pp. 1-9, San Diego, CA, USA, March 2010 (doi: 10.1109/INFCOM.2010.5462154).

[12] A. Tharwat, "Principal component analysis- A tutorial", International Journal of Applied Pattern Recognition, vol. 3, no. 3, Jan. 2016 (doi: 10.1504/IJAPR.2016.079733).

[13] B.Å. Lundvall, C. Rikap, “China's catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems”, Research Policy, vol. 51, no. 1, Article Number: 104395, Jan. 2021.

[14] G. Shobha, S. Rangaswamy, "Chapter 8- Machine learning", Handbook of Statistics, vol. 38, pp. 197-228, 2018 (doi: 10.1016/bs.host.2018.07.004).

[15] K.P. Murphy, "Machine learning: A probabilistic perspective", MIT Press, Cambridge, Massachusetts London, England, 2012 (ISBN-13: 978-0262­01­80­2­9).

[16] J. Qiu, Q. Wu, G. Ding, Y. Xu, S. Feng, "A survey of machine learning for big data processing", EURASIP Journal on Advances in Signal Processing, vol. 2016, no. 1, Article number: 67, May 2016 (doi: 10.1186/s13634-016-0355-x).

[17] A. Mellit, A. Massi Pavan, E. Ogliari, S. Leva, V. Lughi, "Advanced methods for photovoltaic output power forecasting: A review", Applied Sciences, vol. 10, no. 2, Article Number: 487, Jan. 2020 (doi: 10.3390/app10­020487).

[18] S. Shamshirband, T. Rabczuk,K. Chau, "A survey of deep learning techniques: Application in wind and solar energy resources", IEEE Access, vol. 7, pp. 164650-164666, Nov. 2019 (doi: 10.1109/ACCESS.2­019.2­951­750).

[19] Y. LeCun, Y. Bengio, G. Hinton, "Deep learning", Nature, vol. 521, no. 7553, pp. 436-444, 2015 (doi: 10.1038/nature14539).

[20] Y. Xin, L. Kong, Z. Liu, Y. Chen, Y. Li, H. Zhu, M. Gao, H. Hou, C. Wang, "Machine learning and deep learning methods for cybersecurity", IEEE Access, vol. 6, pp. 35365-35381, 2018 (doi: 10.1109/ACCES­S.2018.2836950).

[21] M.A. Wani, F.A. Bhat, S. Afzal, A.I. Khan, "Advances in deep learning", Springer, 2020 (ISBN: 978-981-13-6794-6).

[22] I. Goodfellow, Y. Bengio, A. Courville, "Deep learning", MIT Press, Cambridge, Massachusetts London, England , 2016 (ISBN: 9780262035613).

[23] D. Zhang, X. Han, C. Deng, "Review on the research and practice of deep learning and reinforcement learning in smart grids", Csee Journal of Power and Energy Systems, vol. 4, no. 3, pp. 362-370, Sept. 2018 (doi: 10.17775/CSEEJPES.2018.00520).

[24] Q. Zhang, K. Dehghanpour, Z. Wang, Q. Huang, "A learning-based power management method for networked microgrids under incomplete information", IEEE Trans. on Smart Grid, vol. 11, no. 2, pp. 1193-1204, March 2020 (doi: 10.1109/TSG.2019.2933502).

[25] T. Hastie, R.F. Tishirani, Jerome, "The elements of statistical learning", Springer, 2009 (ISBN-13: 978-0387848570).

[26] A. Dogan, D. Birant, "Machine learning and data mining in manufacturing", Expert Systems with Applications, vol. 166, Article Number: 114060, Mar. 2021 (doi: 10.1016/j.eswa.2020.114060).

[27] V. Garcia-Font, C. Garrigues, H. Rifà-Pous, "A comparative study of anomaly detection techniques for smart city wireless sensor networks", Sensors, vol. 16, no. 6, Article Number: 868, June 2016 (doi: 10.3390/s16060868).

[28] R.S. Sutton, A.G. Barto, "Reinforcement learning: An introduction", MIT Press, Cambridge, Massachusetts London, England, 2018.

[29] L. Yin, Q. Gao, L. Zhao, B. Zhang, T. Wang, S. Li, H. Liu, "A review of machine learning for new generation smart dispatch in power systems", Engineering Applications of Artificial Intelligence, vol. 88, Article Number: 103372, Feb. 2020 (doi: 10.1016/j.engappai.2019.103372).

[30] Y. Pan, "Heading toward artificial intelligence 2.0", Engineering, vol. 2, no. 4, pp. 409-413, Dec. 2016 (doi: 10.1016/J.ENG.2016.04.018).

[31] D. Judith, S. Klamer, O. Serena, "A vision on hybrid AI for military applications", Proceedings  of SPIE, vol. 11169, Strasbourg, France, Oct. 2019(doi: 10.1117/12.2551893).

[32] S. Khan, T. Yairi, "A review on the application of deep learning in system health management", Mechanical Systems and Signal Processing, vol. 107, pp. 241-265, July 2018 (doi: 10.1016/j.ymssp.2017.11.024).

[33] J. Wang, X. Chen, F. Zhang, F. Chen, Y. Xin, "Building load forecasting using deep neural network with efficient feature fusion", Journal of Modern Power Systems and Clean Energy, vol. 9, no. 1, pp. 160-169, Jan. 2021 (doi: 10.35833/MPCE.2020.000321).

[34] S. Zhou, Q. Chen, X. Wang, "Discriminative deep belief networks for image classification", Proceeding of the IEEE/ICIP, pp. 1561-1564, Hong Kong, China, Sept. 2010 (doi: 10.1109/ICIP.2010.5649922).

[35] D. Cao, W. Hu, J. Zhao, G. Zhang, B. Zhang, Z. Liu, Z. Chen, F. Blaabjerg, "Reinforcement learning and its applications in modern power and energy systems: A review", Journal of Modern Power Systems and Clean Energy, vol. 8, no. 6, pp. 1029-1042, Nov. 2020 (doi: 10.35833/MPCE.2020.000552).

[36] Z. Zhang, D. Zhang, R.C. Qiu, "Deep reinforcement learning for power system applications: An overview", Csee Journal of Power and Energy Systems, vol. 6, no. 1, pp. 213-225, March 2020 (doi: 10.17775/CSEE­JPE­S.2019.00920).

[37] G.C. Ruan, H.W. Zhong, G.L. Zhang, Y.L. He, X. Wang, T.J. Pu, "Review of learning-assisted power system optimization", Csee Journal of Power and Energy Systems, Review vol. 7, no. 2, pp. 221-231, March 2021 (doi: 10.17775/cseejpes.2020.03070).

[38] M. Khodayar, J. Wang, "Probabilistic time-varying parameter identification for load modeling: A deep generative approach", IEEE Trans. on Industrial Informatics, vol. 17, no. 3, pp. 1625-1636, March 2021 (doi: 10.11­09/TII.2020.2971014).

[39] J. Liu, F. Qu, X. Hong, H. Zhang, "A small-sample wind turbine fault detection method with synthetic fault data using generative adversarial nets", IEEE Trans. on Industrial Informatics, vol. 15, no. 7, pp. 3877-3888, July 2019 (doi: 10.1109/TII.2018.2885365).

[40] S. Ahmadian, H. Malki, Z. Han, "Cyber attacks on smart energy grids using generative adverserial networks", Proceeding of the IEEE/GlobalSIP, pp. 942-946, Anaheim, CA, USA, Nov. 2018 (doi: 10.1109/GlobalSIP.2018.8646424).

[41] Y. Chen, Y. Wang, D. Kirschen, B. Zhang, "Model-free renewable scenario generation using generative adversarial networks", IEEE Trans. on Power Systems, vol. 33, no. 3, pp. 3265-3275, May 2018 (doi: 10.1109/T­PWR­S.2018.2794541).

[42] Z. Guan, Y. Zhang, L. Zhu, L. Wu, S. Yu, "EFFECT: An efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid", Science China Information Sciences, vol. 62, no. 3, p. 32103, 2019 (doi: 10.1007/s11432-018-9451-y).

[43] C.W. Tsai, C.F. Lai, H.C. Chao, A.V. Vasilakos, "Big data analytics: A survey", Journal of Big Data, vol. 2, no. 1, p. 21, 2015 (doi: 10.1186/s40537-015-0030-3).

[44] Y. Zhang, T. Huang, E.F. Bompard, "Big data analytics in smart grids: A review", Energy Informatics, vol. 1, no. 1, Article Number: 8, 2018 (doi: 10.1186/s42162-018-0007-5).

[45] S. Sagiroglu, R. Terzi, Y. Canbay, I. Colak, "Big data issues in smart grid systems", Proceeding of the IEEE/ICRERA, pp. 1007-1012, Birmingham, UK , Nov. 2016 (doi: 10.1109/ICRERA.2016.7884486).

[46] D. Baimel, S. Tapuchi, N. Baimel, "Smart grid communication technologies- overview, research challenges and opportunities", Proceeding of the IEEE/SPEEDAM, pp. 116-120, Capri, Italy , June 2016 (doi: 10.1109/SPEEDAM.2016.7526014).

[47] H. Daki, A. El Hannani, A. Aqqal, A. Haidine, A. Dahbi, "Big Data management in smart grid: concepts, requirements and implementation", Journal of Big Data, vol. 4, no. 1, Article Number:13, 2017 (doi: 10.1186/s40537-017-0070-y).

[48] J. Ma, J.C.P. Cheng, F. Jiang, W. Chen, M. Wang, C. Zhai, "A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data", Energy and Buildings, vol. 216, Article Number: 109941, 2020 (doi: 10.1016/j.enbuild.2020.109941).

[49] S. Jung, J. Moon, S. Park, S. Rho, S.W. Baik, E. Hwang, "Bagging ensemble of multilayer perceptrons for missing electricity consumption data imputation", Sensors, vol. 20, no. 6, Article Number: 1772, March:2020 (doi: 10.3390/s20061772).

[50] C.L. Stimmel, "Big data analytics strategies for the smart grid", CRC Press, 2019 (ISBN 9781482218282).

[51] D.V. Nga, O.H. See, D.N. Quang, C.Y. Xuen, L.L. Chee, "Visualization techniques in smart grid", Smart Grid and Renewable Energy, vol. 3, no. 3, Article Number: 22056, 2012 (doi: 10.4236/sgre.2012.33025).

[52] L. Xie, Y. Chen, P.R. Kumar, "Dimensionality reduction of synchrophasor data for early event detection: linearized analysis", IEEE Trans. on Power Systems, vol. 29, no. 6, pp. 2784-2794, 2014 (doi: 10.1­109/T­PWR­S.2014.2316476).

[53] S.K. Sharma, X. Wang, "Live data analytics with collaborative edge and cloud processing in wireless iot networks", IEEE Access, vol. 5, pp. 4621-4635, 2017 (doi: 10.1109/ACCESS.2017.2682640).

[54] K. Nguyen, M. Cheriet, "Virtual edge-based smart community network management", IEEE Internet Computing, vol. 20, no. 6, pp. 32-41, 2016 (doi: 10.1109/MIC.2016.127).

[55] X. Yu, Y. Xue, "Smart grids: A cyber–physical systems perspective", Proceedings of the IEEE, vol. 104, no. 5, pp. 1058-1070, 2016 (doi: 10.1109/JPROC.2015.2503119).

[56] S. Bera, S. Misra, J.J.P.C. Rodrigues, "Cloud computing applications for smart grid: A survey", IEEE Trans. on Parallel and Distributed Systems, vol. 26, no. 5, pp. 1477-1494, 2015 (doi: 10.1109/TPDS.2014.2321378).

[57] H. Jahangir, H. Tayarani, S. Baghali, A. Ahmadian, A. Elkamel, M.A. Golkar, M. Castilla, "A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks", IEEE Trans. on Industrial Informatics, vol. 16, no. 4, pp. 2369-2381, 2020 (doi: 10.1109/TII.2019.2933009).

[58] M. Bourdeau, X.Q. Zhai, E. Nefzaoui, X. Guo, P. Chatellier, "Modeling and forecasting building energy consumption: A review of data-driven techniques", Sustainable Cities and Society, vol. 48, Article Number: 101533, 2019 (doi: 10.1016/j.scs.2019.101533).

[59] B. Luo, S. Miao, "A literature survey on electricity price forecasting in deregulated markets", Proceeding of the IEEE/iSPEC, pp. 359-365,  Beijing, China , Nov. 2019 (doi: 10.1109/iSPEC48194.2019.8974939).

[60] M. Askari, F. Keynia, "Mid-term electricity load forecasting by a new composite method based on optimal learning MLP algorithm", IET Generation, Transmission and Distribution, vol. 14, no. 5, pp. 845-852, March 2020 (doi: 10.1049/iet-gtd.2019.0797).

[61] F. Ziel, R. Steinert, "Probabilistic mid- and long-term electricity price forecasting", Renewable and Sustainable Energy Reviews, vol. 94, pp. 251-266, 2018 (doi: 10.1016/j.rser.2018.05.038).

[62] M. Tan, S. Yuan, S. Li, Y. Su, H. Li, F. He, "Ultra-short-term industrial power demand forecasting using lstm based hybrid ensemble learning", IEEE Trans. on Power Systems, vol. 35, no. 4, pp. 2937-2948, 2020 (doi: 10.1109/TPWRS.2019.2963109).

[63] A.A. Mamun, M. Sohel, N. Mohammad, M.S.H. Sunny, D.R. Dipta, E. Hossain, "A comprehensive review of the load forecasting techniques using single and hybrid predictive models", IEEE Access, vol. 8, pp. 134911-134939, 2020 (doi: 10.1109/ACCESS.2020.3010702).

[64] C. Kuster, Y. Rezgui, M. Mourshed, "Electrical load forecasting models: A critical systematic review", Sustainable Cities and Society, vol. 35, pp. 257-270, 2017 (doi: 10.1016/j.scs.2017.08.009).

[65] Y.H. Chen, W.C. Hong, W. Shen, N.N. Huang, "Electric load forecasting based on a least squares support vector machine with fuzzy time series and global harmony search algorithm", Energies, vol. 9, no. 2, Article Number: 70, Jan. 2016 (doi: 10.3390/en9020070).

[66] D. Niu, S. Dai, "A short-term load forecasting model with a modified particle swarm optimization algorithm and least squares support vector machine based on the denoising method of empirical mode decomposition and grey relational analysis", Energies, vol. 10, no. 3, Article Number: 408, March 2017 (doi: 10.3390/en10­030408).

[67] L. Suganthi, A.A. Samuel, "Energy models for demand forecasting- A review", Renewable and Sustainable Energy Reviews, vol. 16, no. 2, pp. 1223-1240, 2012 (doi: 10.1016/j.rser.2011.08.014).

[68] S. Welikala, C. Dinesh, M.P.B. Ekanayake, R.I. Godaliyadda, J. Ekanayake, "Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting", IEEE Trans. on Smart Grid, vol. 10, no. 1, pp. 448-461, 2019 (doi: 10.1109/TSG.2017.2743760).

[69] S. Singh, A. Yassine, "Mining energy consumption behavior patterns for households in smart grid", IEEE Trans. on Emerging Topics in Computing, vol. 7, no. 3, pp. 404-419, 2019 (doi: 10.1109/TETC.2­017.2­6920­9­8).

[70] S. Oprea, A. Bâra, "Machine learning algorithms for short-term load forecast in residential buildings using smart meters, sensors and big data solutions", IEEE Access, vol. 7, pp. 177874-177889, 2019 (doi: 10.1109/A­CCE­SS.2019.2958383).

[71] I. Kiprijanovska, S. Stankoski, I. Ilievski, S. Jovanovski, M. Gams, H. Gjoreski, "HousEEC: day-ahead household electrical energy consumption forecasting using deep learning", Energies, vol. 13, no. 10, Article Number: 2672, May 2020 (doi: 10.3390/en13102672).

[72] D. Wei, J. Wang, K. Ni, G. Tang, "Research and application of a novel hybrid model based on a deep neural network combined with fuzzy time series for energy forecasting", Energies, vol. 12, no. 18, Article Number: 3588, Sept. 2019 (doi: 10.3390/en12183588).

[73] Z. Guo, K. Zhou, X. Zhang, S. Yang, "A deep learning model for short-term power load and probability density forecasting", Energy, vol. 160, pp. 1186-1200, 2018 (doi: 10.1016/j.energy.2018.07.090).

[74] S. K. Acharya, Y.-M. Wi, J. Lee, "Short-term load forecasting for a single household based on convolution neural networks using data augmentation", Energies, vol. 12, no. 18, Article Number: 3560, Sept. 2019 (doi: 10.3390/en12183560).

[75] H. Chen, S. Wang, S. Wang, Y. Li, "Day-ahead aggregated load forecasting based on two-terminal sparse coding and deep neural network fusion", Electric Power Systems Research, vol. 177, Article Number: 105987, 2019 (doi: 10.1016/j.epsr.2019.105987).

[76] S. Mujeeb, N. Javaid, M. Ilahi, Z. Wadud, F. Ishmanov, M. K. Afzal, "Deep long short-term memory: A new price and load forecasting scheme for big data in smart cities", Sustainability, vol. 11, no. 4, Article Number: 987, Feb. 2019 (doi: 10.3390/su11040987).

[77] Y. Yang, W. Li, T.A. Gulliver, S. Li, "Bayesian deep learning-based probabilistic load forecasting in smart grids", IEEE Trans. on Industrial Informatics, vol. 16, no. 7, pp. 4703-4713, 2020 (doi: 10.1109/TII.2­019.2­94­2353).

[78] W. Kong, Z.Y. Dong, Y. Jia, D.J. Hill, Y. Xu, Y. Zhang, "Short-term residential load forecasting based on lstm recurrent neural network", IEEE Trans. on Smart Grid, vol. 10, no. 1, pp. 841-851, 2019 (doi: 10.1109/TS­G.2017.2753802).

[79] T. Ahmad, H. Zhang, B. Yan, "A review on renewable energy and electricity requirement forecasting models for smart grid and buildings", Sustainable Cities and Society, vol. 55, Article Number:102052, 2020 (doi: 10.1016/j.scs.2020.102052).

[80] M. Sharifzadeh, A. Sikinioti-Lock, N. Shah, "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression", Renewable and Sustainable Energy Reviews, vol. 108, pp. 513-538, July 2019 (doi: 10.101­6/j.­rse­r.2­019.03.040).

[81] B. Chen, P. Lin, Y. Lai, S. Cheng, Z. Chen, L. Wu, "Very-short-term power prediction for pv power plants using a simple and effective rcc-lstm model based on short term multivariate historical datasets", Electronics, vol. 9, no. 2, Article Number: 289, Feb. 2020 (doi: 10.3390/electronics9020289).

[82] M. Abdel-Nasser, K. Mahmoud, "Accurate photovoltaic power forecasting models using deep LSTM-RNN", Neural Computing and Applications, vol. 31, no. 7, pp. 2727-2740, 2019 (doi: 10.1007/s00521-017-3225-z).

[83] X. Qing, Y. Niu, "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM", Energy, vol. 148, pp. 461-468, 2018 (doi: 10.1016/j.energy.2018.01.177).

[84] H. Zhou, Y. Zhang, L. Yang, Q. Liu, K. Yan, Y. Du, "Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism", IEEE Access, vol. 7, pp. 78063-78074, 2019 (doi: 10.1109/ACCESS.2019.2923006).

[85] B. Zhou, X. Ma, Y. Luo, D. Yang, "Wind power prediction based on LSTM networks and nonparametric kernel density estimation", IEEE Access, vol. 7, pp. 165279-165292, 2019 (doi: 10.1109/ACCESS.2­019.2­952­555).

[86] H.Z. Wang, G.Q. Li, G.B. Wang, J.C. Peng, H. Jiang, Y.T. Liu, "Deep learning based ensemble approach for probabilistic wind power forecasting", Applied Energy, vol. 188, pp. 56-70, 2017 (doi: 10.1016/j.apen­ergy.2­016.11.111).

[87] Y. Ju, G. Sun, Q. Chen, M. Zhang, H. Zhu, M.U. Rehman, "A model combining convolutional neural network and lightgbm algorithm for ultra-short-term wind power forecasting", IEEE Access, vol. 7, pp. 28309-28318, 2019 (doi: 10.1109/ACCESS.2019.2901920).

[88] J. Jeong, H. Kim, "Multi-site photovoltaic forecasting exploiting space-time convolutional neural network", Energies, vol. 12, no. 23, Article Number: 4490, Nov. 2019 (doi: 10.3390/en12234490).

[89] M.Q. Raza, N. Mithulananthan, J. Li, K.Y. Lee, H.B. Gooi, "An ensemble framework for day-ahead forecast of pv output power in smart grids", IEEE Trans. on Industrial Informatics, vol. 15, no. 8, pp. 4624-4634, 2019 (doi: 10.1109/TII.2018.2882598).

[90] D. Lee, K. Kim, "Recurrent neural network-based hourly prediction of photovoltaic power output using meteorological information", Energies, vol. 12, no. 2, Article Number: 215, Jan. 2019 (doi: 10.3390/en12­020215).

[91] F. Zhang, H. Fleyeh, "A review of single artificial neural network models for electricity spot price forecasting", Proceeding of the IEEE/EEM, pp. 1-6, 2019 (doi: 10.1109/EEM.2019.8916423).

[92] P.H. Kuo, C.J. Huang, "An electricity price forecasting model by hybrid structured deep neural networks", Sustainability, vol. 10, no. 4, Article Number: 1280, April 2018 (doi: 10.3390/su10041280).

[93] G. Díaz, J. Coto, J. Gómez-Aleixandre, "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression", Applied Energy, vol. 239, pp. 610-625, 2019 (doi: 10.1016/j.apenergy.2019.01.213).

[94] U. Ugurlu, I. Oksuz, O. Tas, "Electricity price forecasting using recurrent neural networks", Energies, vol. 11, no. 5, Article Number: 1255, May 2018 (doi: 10.3390/en11051255).

[95] J. Lago, F.D. Ridder, B.D. Schutter, "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms", Applied Energy, vol. 221, pp. 386-405, 2018 (doi: 10.10­16/j.ap­energy.2018.02.069).

[96] J. Wang, F. Liu, Y. Song, J. Zhao, "A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system", Applied Soft Computing, vol. 48, pp. 281-297, 2016 (doi: 10.1016/­j.aso­c.2016.07.011).

[97] R. Khalid, N. Javaid, F.A. Al-zahrani, K. Aurangzeb, E.U.H. Qazi, T. Ashfaq, "Electricity load and price forecasting using jaya-long short term memory (JLSTM) in smart grids", Entropy, vol. 22, no. 1, Article Number: 10, Dec.2020 (doi: 10.3390/e22010010).

[98] L. Gigoni, A. Betti, M. Tucci, E. Crisostomi, "A scalable predictive maintenance model for detecting wind turbine component failures based on SCADA data", Proceeding of the IEEE/PESGM, pp. 1-5, Atlanta, GA, USA , Aug. 2019 (doi: 10.1109/PESGM40551.2019.8973898).

[99] R. Venkataswamy, K.U. Rao, P. Meena, "Internet of things based metaheuristic reliability centered maintenance of distribution transformers", IOP Conference Series: Earth and Environmental Science, vol. 463, no. 1, Article Number: 012047, 2020 (doi: 10.1088/1755-1315/463/1/012047).

[100] T.P. Carvalho, F.A.A.M.N. Soares, R. Vita, R.P. Francisco, J.P. Basto, S.G.S. Alcalá, "A systematic literat­ure review of machine learning methods applied to predictive maintenance", Computers and Industrial Engineering, vol. 137, Article Number: 106024, Nov. 2019 (doi: 10.1016/j.cie.2019.106024).

[101] F. Kabir, B. Foggo, N. Yu, "Data driven predictive maintenance of distribution transformers", Proceeding of the IEEE/CICED, pp. 312-316, Tianjin, China, Sept.  2018 (doi: 10.1109/CICED.2018.8592417).

[102] T. Huuhtanen, A. Jung, "Predictive maintenance of photovoltaic panels via deep learning", Proceeding of the IEEE/DSW, pp. 66-70, Lausanne, Switzerland, June 2018 (doi: 10.1109/DSW.2018.8439898).

[103] M.R. Skydt, M. Bang, H.R. Shaker, "A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks", Measurement, vol. 170, Article Number: 108691, Jan. 2021 (doi: 10.1016/j.measurement.2020.108691).

[104] M. Nithya, S. Nagarajan, P. Navaseelan, "Fault detection of wind turbine system using neural networks", Proceeding of the IEEE/TIAR, pp. 103-108, Chennai, India, April 2017 (doi: 10.1109/TIAR.2017.8273694).

[105] R. Tervo, J. Karjalainen, A. Jung, "Predicting electricity outages caused by convective storms", Proceeding of the IEEE/DSW, pp. 145-149, Lausanne, Switzerland, June 2018 (doi: 10.1109/DSW.2018.8439906).

[106] G. Kaikai, C. Kai, G. Ran, W. Ruoyue, P. Zhonghan, S. You, "Typical ice-wind fault analysis based on correlation analysis and naive bayesian method", Proceeding of the IEEE/ICET, pp. 372-377, Chengdu, China , May 2020 (doi: 10.1109/ICET49382.2020.9119709).

[107] Y. Li, X. Song, S. Zhao, F. Gao, "A line-fault cause analysis method for distribution network based on decision-making tree and machine learning", Proceeding of the IEEE/ACPEE, pp. 1-5, Chengdu, China, June 2020 (doi: 10.1109/ACPEE48638).

[108] C. Cepeda, C. Orozco-Henao, W. Percybrooks, J.D. Pulgarín-Rivera, O.D. Montoya, W. Gil-González, J.C. Vélez, "Intelligent fault detection system for microgrids", Energies, vol. 13, no. 5, Article Number: 1223, March 2020 (doi: 10.3390/en13051223).

[109] J.Y. Hsu, Y. Wang, K.C. Lin, M. Chen, J.H.Y. Hsu, "Wind turbine fault diagnosis and predictive maint­enance through statistical process control and machine learning", IEEE Access, vol. 8, pp. 23427-23439, Jan. 2020 (doi: 10.1109/ACCESS.2020.2968615).

[110] M.A. Karim, J. Currie, T. Lie, "Dynamic event detection using a distributed feature selection based machine learning approach in a self-healing microgrid", IEEE Trans. on Power Systems, vol. 33, no. 5, pp. 4706-4718, Sept. 2018 (doi: 10.1109/TPWRS.2018.2812768).

[111] W. Li, D. Deka, M. Chertkov, M. Wang, "Real-time faulted line localization and pmu placement in power systems through convolutional neural networks", IEEE Trans. on Power Systems, vol. 34, no. 6, pp. 4640-4651, Nov. 2019 (doi: 10.1109/TPWRS.2019.2917794).

[112] Y. Huo, G. Prasad, L. Lampe, V.C.M. Leung, "Advanced smart grid monitoring: intelligent cable diagnostics using neural networks", Proceeding of the IEEE/ISPLC, Malaga, Spain, pp. 1-6, May 2020 (doi: 10.1109­/IS­PLC­48789.2020.9115403).

[113] E. Garoudja, A. Chouder, K. Kara, S. Silvestre, "An enhanced machine learning based approach for failures detection and diagnosis of PV systems", Energy Conversion and Management, vol. 151, pp. 496-513, Nov. 2017 (doi: 10.1016/j.enconman.2017.09.019).