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

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• 作者：Jan Janou&scaron ; ek ^{jan.janousek@vsb.cz" class="auth_mail" title="E-mail the corresponding author} ; Petr Gajdo&scaron ; Pavel Dohná ; lek ; ^{pavel.dohnalek@vsb.cz" class="auth_mail" title="E-mail the corresponding author} ; Michal Radecký
• 关键词：Random Forests ; Regression ; GPU ; CUDA ; Parallel computing
• 刊名：Swarm and Evolutionary Computation
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：26
• 期：Complete
• 页码：50-55
• 全文大小：1395 K

文摘

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.