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dc.contributor.author | Kalimoldayev, Maksat | |
dc.contributor.author | Drozdenko, Aleksey | |
dc.contributor.author | Koplyk, Igor | |
dc.contributor.author | Marinich, T. | |
dc.contributor.author | Abdildayeva, Assel | |
dc.contributor.author | Zhukabayeva, Tamara | |
dc.date.accessioned | 2024-10-09T06:37:37Z | |
dc.date.available | 2024-10-09T06:37:37Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 2629-4885 | |
dc.identifier.other | doi.org/10.1515/eng-2020-0028 | |
dc.identifier.uri | http://rep.enu.kz/handle/enu/17531 | |
dc.description.abstract | A review of modern methods of forming a mathematical model of power systems and the development of an intelligent information system for monitoring electricity consumption. The main disadvantages and advantages of the existing modeling approaches , as well as their applicability to the energy systems of Ukraine and Kazakhstan,are identified. The main factors that affect the dynamics of energy consumption are identified. A list of the main tasks that need to be implemented in order to develop algorithms for predicting electricity demand for various objects, industries and levels has been developed. | ru |
dc.language.iso | en | ru |
dc.publisher | De Gruyter | ru |
dc.relation.ispartofseries | 10;:350–361 | |
dc.subject | prediction | ru |
dc.subject | power consumption | ru |
dc.subject | panel models | ru |
dc.subject | autoregression models | ru |
dc.subject | neural networks | ru |
dc.title | Analysis of modern approaches for the prediction of electric energy consumption | ru |
dc.type | Article | ru |