Short-term load forecasting with clustering–regression model in distributed cluster View Full Text


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Article Info

DATE

2017-09-22

AUTHORS

Jingsheng Lei, Ting Jin, Jiawei Hao, Fengyong Li

ABSTRACT

This paper tackles a new challenge in power big data: how to improve the precision of short-term load forecasting with large-scale data set. The proposed load forecasting method is based on Spark platform and “clustering–regression” model, which is implemented by Apache Spark machine learning library (MLlib). Proposed scheme firstly clustering the users with different electrical attributes and then obtains the “load characteristic curve of each cluster”, which represents the features of various types of users and is considered as the properties of a regional total load. Furthermore, the “clustering–regression” model is used to forecast the power load of the certain region. Extensive experiments show that the proposed scheme can predict reasonably the short-term power load and has excellent robustness. Comparing with the single-alone model, the proposed method has a higher efficiency in dealing with large-scale data set and can be effectively applied to the power load forecasting. More... »

PAGES

10163-10173

References to SciGraph publications

  • 2015. Short-Term Load Forecasting Using Random Forests in INTELLIGENT SYSTEMS'2014
  • 2016-07-16. Medium and Long-Term Electric Power Planning Load Forecasting Based on Variable Weights Gray Model in PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND AUTOMATIC CONTROL
  • 2015-10-28. A distributed frequent itemset mining algorithm using Spark for Big Data analytics in CLUSTER COMPUTING
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    http://scigraph.springernature.com/pub.10.1007/s10586-017-1198-4

    DOI

    http://dx.doi.org/10.1007/s10586-017-1198-4

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