Load Forecasting of Power SCADA Based on Spark MLlib

DOI

10.2991/msota-16.2016.108How to use a DOI?

Keywords

component; spark; decision tree; random forest; k-menas

Abstract

In order to improve the accuracy and speed of power forecasting in power SCADA system, a distributed real-time steaming forecasting model is designed based on K-means algorithm and Random Forest algorithm in the Spark machine learning library (MLlib). The model uses the sliding window mechanism to segment the incoming data stream. K-means Clustering is used to correct the abnormally data, and then the Random Forest Regression forecasting is performed. Model algorithms is implemented based on the Spark RDD, the performance of the algorithm is verified by sending the data through the daemon process which is a simulation of the message queue. The results show that the forecasting accuracy of the algorithm is superior to the traditional serial Random Forest forecasting and satisfies the real-time requirement.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tao Lin
AU  - Chong Jiang
PY  - 2016/12
DA  - 2016/12
TI  - Load Forecasting of Power SCADA Based on Spark MLlib
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 480
EP  - 484
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.108
DO  - 10.2991/msota-16.2016.108
ID  - Lin2016/12
ER  -