Towards power plant output modelling and optimization using parallel Regression Random Forest

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86096022" target="_blank" >RIV/61989100:27240/16:86096022 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.swevo.2015.07.004" target="_blank" >http://dx.doi.org/10.1016/j.swevo.2015.07.004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.swevo.2015.07.004" target="_blank" >10.1016/j.swevo.2015.07.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards power plant output modelling and optimization using parallel Regression Random Forest

Popis výsledku v původním jazyce

In this paper, we explore the possibilities of using the Random Forest algorithm in its regression version to predict the power output of a power plant based on hourly measured data. This is a task commonly leading to a optimization problem that is, in general, best solved using a bio-inspired technique. We extend the results already published on this topic and show that Regression Random Forest can be a better alternative to solve the problem. A comparison of the method with previously published results is included. In order to implement the algorithm in a way that is as efficient as possible, a massively parallel implementation using a Graphics Processing Unit was used and is also described.

Název v anglickém jazyce

Towards power plant output modelling and optimization using parallel Regression Random Forest

Popis výsledku anglicky

In this paper, we explore the possibilities of using the Random Forest algorithm in its regression version to predict the power output of a power plant based on hourly measured data. This is a task commonly leading to a optimization problem that is, in general, best solved using a bio-inspired technique. We extend the results already published on this topic and show that Regression Random Forest can be a better alternative to solve the problem. A comparison of the method with previously published results is included. In order to implement the algorithm in a way that is as efficient as possible, a massively parallel implementation using a Graphics Processing Unit was used and is also described.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Swarm and Evolutionary Computation

ISSN

2210-6502

e-ISSN

—

Svazek periodika

26

Číslo periodika v rámci svazku

Ferbruary

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

50-55

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84959529176