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 |