MATEC Web Conf.
Volume 228, 20182018 3rd International Conference on Circuits and Systems (CAS 2018)
|Number of page(s)||4|
|Section||Intelligent Computing and Information Processing|
|Published online||14 November 2018|
Research on Random Forest Algorithm Based on Big Data in Parallel Load Forecasting
Luoyang Normal College, Luoyang Henan 471934 China
The paper propose a parallel load forecasting method based on random forest algorithm, through the analysis of historical load, temperature, wind speed and other data, the algorithm can shorten the load forecasting time and improve the processing capability of large data. This paper also designs and implements parallel load forecasting prototype system based on power user side large data of a Hadoop, including data cluster management, data management, prediction classification algorithm library and other functions. The experimental results show that the accuracy of parallel stochastic forest algorithm is obviously higher than decision tree, and the prediction accuracy on the different data sets is generally higher than decision tree, and it can better analyze and process large data.
Key words: Power user side / Parallel processing / Load forecasting / Large data
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.