Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach

Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutio...

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Bibliographic Details
Published inInternational Conference on Big Data and Smart Computing pp. 119 - 125
Main Authors Xishuang Dong, Lijun Qian, Lei Huang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2017
Subjects
Big Data Analytics
Convolutional Neural Network
Data models
Forecasting
Load forecasting
Load modeling
Machine Learning
Predictive models
Short-term Load Forecasting
Testing
Training
Online AccessGet full text
ISSN2375-9356
DOI10.1109/BIGCOMP.2017.7881726

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Summary:Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutional neural network with K-means clustering is proposed for short-term load forecasting with improved scalability. The large data set is clustered into subsets using K-means algorithm, then the obtained subsets are used to train the convolutional neural network. A real-world power industry data set containing more than 1.4 million of load records is used in this study and the experimental results demonstrate the effectiveness of the proposed method.
ISSN:2375-9356
DOI:10.1109/BIGCOMP.2017.7881726