Supervised Learning of Photovoltaic Power Plant Output Prediction Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86088091" target="_blank" >RIV/61989100:27240/13:86088091 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/13:86088091

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Supervised Learning of Photovoltaic Power Plant Output Prediction Models

Original language description

This study presents an application of evolutionary fuzzy rules to the modeling and prediction of power output of a real-world Photovoltaic Power Plant (PVPP). The method is compared to arti cial neural networks and support vector regression that were also used to build predictors in order to analyze a time-series like data describing the production of the PVPP. The models of the PVPP are created using di ferent supervised machine learning methods in order to forecast the short-term output of the power plant and compare the accuracy of the prediction.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JE - Non-nuclear power engineering, energy consumption and utilization

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2013

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Network World

ISSN

1210-0552

e-ISSN

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Volume of the periodical

23

Issue of the periodical within the volume

4

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

18

Pages from-to

321-338

UT code for WoS article

000325193300004

EID of the result in the Scopus database

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