Using Machine Learning Algorithms to Detect Anomalies in the Solar Heating System

e3fda188-0d35-4bd1-85a0-cb63652d220b20220126040011034naun:naunmdt@crossref.orgMDT DepositInternational Journal of Mechanics1998-444810.46300/9104http://www.naun.org/cms.action?id=2828128202112820211510.46300/9104.2021.15https://www.naun.org/cms.action?id=23280Using Machine Learning Algorithms to Detect Anomalies in the Solar Heating SystemMuratKunelbayevAl Farabi Kazakh National University, Institute of Information and Computer Technologies, Almaty, 05000, KazakhstanAbdildayevaAsselAl-Farabi Kazakh National Universitу, Almaty, 05000, KazakhstanTaganovaGuldanaL.N.Gumilov Eurasian National University, Nur-Sultan, 05000, KazakhstanThis article explores the use of machine learning algorithms to identify anomalies in the solar heating system. A solar heating system that has been developed consists of several parts to simplify the description and modeling process. The authors propose a new architecture for neural networks based on ordinary differential equations. The idea is to apply the new architecture for practical problems of accident prediction (the problem of extrapolation of time series) and classification (classification of accidents based on historical data). The developed machine learning algorithms, artificial intelligence techniques, the theory of differential equations - these directions allow us to build a model for predicting the system's accident rate. The theory of database management (non-relational databases) - these systems allow you to establish the optimal storage of large time series.123120211231202127027532https://www.naun.org/main/NAUN/mechanics/2021/a642003-032(2021).pdf10.46300/9104.2021.15.32https://www.naun.org/main/NAUN/mechanics/2021/a642003-032(2021).pdfRENA, 2018. Opportunities to accelerate national energy transitions through advanced deployment of renewables Niu, G., 2017. Data-Driven Technology for Engineering Systems Health Management. Springer Singapore, Singapore. Lui, Y., Zhao, G., Peng, X., Hu, C., 2017. Lithiumion battery remaining useful life prediction with long short-term memory recurrent neural network. Annu. Conf. Progn. Heal. Manag. Soc. 1–7. 10.1016/j.solener.2011.03.025de Keizer, A.C., Vajen, K., Jordan, U., 2011. Review of long-term fault detection approaches in solar thermal systems. Sol. Energy 85, 1430–1439. 10.1016/j.rser.2018.01.001Ghritlahre, H.K., Prasad, R.K., 2018. Application of ANN technique to predict the performance of solar collector systems - A review. Renew. Sustain. Energy Rev. 84, 75–88. 10.1016/j.solener.2007.06.010Kalogirou, S., Lalot, S., Florides, G., Desmet, B., 2008. Development of a neural network-based fault diagnostic system for solar thermal applications. Sol. Energy 82, 164–172. Lipton, Z.C., Berkowitz, J., Elkan, C., 2015. A Critical Review of Recurrent Neural Networks for Sequence Learning. Proc. ACM Int. Conf. Multimed. - MM ’14 675–678. 10.1016/j.solener.2018.01.005rivastava, S., Lessmann, S., 2018. A comparative study of LSTM neural networks in forecasting dayahead global horizontal irradiance with satellite data. Sol. Energy 162, 232–247. 10.1016/j.matcom.2015.05.010Leva, S., Dolara, A., Grimaccia, F., Mussetta, M., Ogliari, E., 2017. Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power. Math. Comput. Simul. 131, 88–100. 10.1016/j.renene.2019.07.100Correa-Jullian, C., Cardemil, J.M., Droguett, E.L., Behzad, M., 2019. Assessment of Deep Learning Techniques for Prognosis of Solar Thermal Systems. Renew. Energy 145, 2178–2191. 10.1016/j.renene.2018.12.086Kalogirou, S.A., Agathokleous, R., Barone, G., Buonomano, A., Forzano, C., Palombo, A., 2019. Development and validation of a new TRNSYS Type for thermosiphon flat-plate solar thermal collectors: energy and economic optimization for hot water production in different climates. Renew. Energy 136, 632–644. 10.1016/j.renene.2008.09.007Souliotis, M., Kalogirou, S., Tripanagnostopoulos, Y., 2009. Modelling of an ICS solar water heater using artificial neural networks and TRNSYS. Renew. Energy 34, 1333–1339. Keshuov, S., Omarov, R., Tokmoldayev, A., Omar, D., Kunelbayev, M., Amirseit, S. Hybrid system for using renewable sources of energy for local consumers in agriculture 2017, Journal of Engineering and Applied Sciences , (5), 1296-1306 10.37394/23209.2020.17.5Djamila Bouhalouan, Bakhta Nachet, Abdelkader Adla, "A Knowledge-Intensive Decision Support System for Industrial Machines Maintenance", WSEAS Transactions on Information Science and Applications, pp. 41-52, Volume 17, 2020 10.37394/232011.2020.15.24Mahmoud Mohamed El-Ghobashy El-Hagar, "Simple and Efficient Methods to Perform the Optimum Matching between a Reciprocating Piston Pump and a Wind Machine", WSEAS Transactions on Applied and Theoretical Mechanics, pp. 216-221 Volume 15, 2020.