Класифікація методів машинного навчання у MicroGrid

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dc.contributor.authorКрилов, А. В.
dc.date.accessioned2023-03-29T08:19:48Z
dc.date.available2023-03-29T08:19:48Z
dc.date.issued2020
dc.description.abstractУ статті розглянуто класифікацію й застосування методів машинного навчання в системі розподіленої генерації енергії MicroGrid. Проведено порівняльний аналіз існуючих методів машинного навчання та їх відповідність задачам керування у MicroGrid. Проведено розробку програмної складової реалізації дерева прийняття рішень для розв’язання задачі класифікації даних машинного навчання.uk
dc.description.abstractotherUse of Internet of Things (IoT) concept plays the important role in modern electrotechnical systems with distributed generation. In particular, for the objects like “smart house” in the bounds of this concept some processes are considered that provide fulfillment of such control and regulation functions like following: light control; control of electrical energy consumption; monitoring of biotelemetrical parameters. Of course, this list is far from the complete one. Additionally, the range of control tasks depends on functionality and features of the system. The more is range of the tasks and number of different parameters to be measured, analyzed and processed – the more urgent become use of complicated control algorithms. Among them methods of artificial intelligent has the important place. The actuality is additionally proven by the further increasing of sales volume of the smartphones and other gadgets that combine more and more sensors and give more and more possibilities for the learning, getting and analyzing of the huge volume of different-kind data. Modern systems of energy consumption control use automated regulation of the devices and automated process of decision making. During this, demands of the end consumer get more priority because of necessity to provide admirable level of “comfortability” while all demands of safety and life support should be answered too. However, the presence of the human like a consumer of the services of smart house leads to the complexity of control algorithms creation due to subjectivity and impossibility to predict human behavior. Thus, the methods that can more or less imitate human behavior in technical system have the huge perspective. The aim of the paper is investigation of complicated intellectual methods of decisionmaking in MicroGrid generation as well as creation of the method able to classify working regimes in MicroGrid. In machine learning methods it is highly important to provide necessary quality of the learning based on accumulated data sets. This quality depends not only on characteristics of dataset itself (like completeness and level of compensation) but also on the learning technique. There is classification of the learning methods was fulfilled in the paper. Among them we considered methods based on following concepts: symbolism; connectivity; evolution; Bayesian probability; analogies. For each method and each ideology we pointed the idea, the concept, the approach and the most problem. Such classification allows to understand better what kind of learning is more preferable on this or that stage or in this or that situation. Then correspondence between the machine learning methods and control tasks in MicroGrid were investigated and presented. The software for the simple situation of light and heating control were elaborated and presented in the paper. Visualization of the machine learning result were executed and the areas of different classes were defined and shown.uk
dc.format.pagerangeС. 34-37uk
dc.identifier.citationКрилов, А. В. Класифікація методів машинного навчання у MicroGrid / Крилов А. В. // Електронна та Акустична Інженерія : науково-технічний журнал. – 2020. – Т. 3, № 2. – С. 34-37. – Бібліогр.: 9 назв.uk
dc.identifier.doihttps://doi.org/10.20535/2617-0965.2020.3.2.200576
dc.identifier.orcid0000-0002-7203-300Xuk
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/54087
dc.language.isoukuk
dc.publisherКПІ ім. Ігоря Сікорськогоuk
dc.publisher.placeКиївuk
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceЕлектронна та Акустична Інженерія : науково-технічний журнал, 2020, Т. 3, № 2
dc.subjectMicroGriduk
dc.subjectмашинне навчанняuk
dc.subjectдерево прийняття рішеньuk
dc.subjectMicroGriduk
dc.subjectmachine learninguk
dc.subjectdecision-making treeuk
dc.subject.udc621:314uk
dc.titleКласифікація методів машинного навчання у MicroGriduk
dc.typeArticleuk

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Електронна та Акустична Інженерія: науково-технічний журнал, Т. 3, № 2