Predicting smart cities’ electricity demands using k-means clustering algorithm in smart grid

This work aims to perform the unified management of various departments engaged in smart city construction by big data, establish a synthetic data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. A new electricity demand predi...

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Published in:Computer Science and Information Systems Vol. 20; no. 2; pp. 657 - 678
Main Authors: Wang, Shurui, Song, Aifeng, Qian, Yufeng
Format: Journal Article
Language:English
Published: 01.04.2023
ISSN:1820-0214, 2406-1018
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Abstract This work aims to perform the unified management of various departments engaged in smart city construction by big data, establish a synthetic data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. A new electricity demand prediction model based on back propagation neural network (BPNN) is proposed for China?s electricity industry according to the smart city?s big data characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form a big intelligent database. Moreover, the K-means clustering algorithm mines and analyzes the data to optimize the power consumers? information. The BPNN model is used to extract features for prediction. Users with weak daily correlation obtained by the K-means clustering algorithm only input the historical load of adjacent moments into the BPNN model for prediction. Finally, the electricity market is evaluated by exploring the data correlation in-depth to verify the proposed model?s effectiveness. The results indicate that the K-mean algorithm can significantly improve the segmentation accuracy of power consumers, with a maximum accuracy of 85.25% and average accuracy of 83.72%. The electricity consumption of different regions is separated, and the electricity consumption is classified. The electricity demand prediction model can enhance prediction accuracy, with an average error rate of 3.27%. The model?s training significantly speeds up by adding the momentum factor, and the average error rate is 2.13%. Therefore, the electricity demand prediction model achieves high accuracy and training efficiency. The findings can provide a theoretical and practical foundation for electricity demand prediction, personalized marketing, and the development planning of the power industry.
AbstractList This work aims to perform the unified management of various departments engaged in smart city construction by big data, establish a synthetic data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. A new electricity demand prediction model based on back propagation neural network (BPNN) is proposed for China?s electricity industry according to the smart city?s big data characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form a big intelligent database. Moreover, the K-means clustering algorithm mines and analyzes the data to optimize the power consumers? information. The BPNN model is used to extract features for prediction. Users with weak daily correlation obtained by the K-means clustering algorithm only input the historical load of adjacent moments into the BPNN model for prediction. Finally, the electricity market is evaluated by exploring the data correlation in-depth to verify the proposed model?s effectiveness. The results indicate that the K-mean algorithm can significantly improve the segmentation accuracy of power consumers, with a maximum accuracy of 85.25% and average accuracy of 83.72%. The electricity consumption of different regions is separated, and the electricity consumption is classified. The electricity demand prediction model can enhance prediction accuracy, with an average error rate of 3.27%. The model?s training significantly speeds up by adding the momentum factor, and the average error rate is 2.13%. Therefore, the electricity demand prediction model achieves high accuracy and training efficiency. The findings can provide a theoretical and practical foundation for electricity demand prediction, personalized marketing, and the development planning of the power industry.
Author Qian, Yufeng
Wang, Shurui
Song, Aifeng
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Cites_doi 10.1080/10438599.2017.1322234
10.1016/j.rser.2020.109725
10.1186/s13673-018-0136-7
10.1016/j.apenergy.2016.12.134
10.1016/j.egypro.2017.05.068
10.3390/s19112485
10.1016/j.energy.2020.117948
10.1016/j.egypro.2017.12.624
10.3390/en12050931
10.1016/j.energy.2018.10.012
10.3389/fncom.2017.00024
10.1109/MCOM.2017.1600267CM
10.1080/0951192X.2017.1314015
10.1007/s00450-017-0360-9
10.1016/j.knosys.2018.01.031
10.1039/C6JA00322B
10.1007/s12667-016-0203-y
10.22214/ijraset.2017.4132
10.30534/ijeter/2020/71892020
10.1016/j.patcog.2017.06.023
10.1038/nrneurol.2016.187
10.1016/j.enpol.2018.11.047
10.1007/s00521-016-2645-5
10.1016/j.energy.2018.07.084
10.3389/fnins.2017.00324
10.1109/TCSII.2019.2891704
10.1186/s13673-018-0125-x
10.1016/j.cities.2016.10.005
10.1016/j.tics.2018.12.005
10.1016/j.enbuild.2017.09.082
10.1016/j.epsr.2016.08.031
10.1109/ACCESS.2019.2939595
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References ref13
ref12
ref34
ref15
ref14
ref31
ref30
ref11
ref33
ref10
ref32
ref2
ref1
ref17
ref16
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref23
  doi: 10.1080/10438599.2017.1322234
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  doi: 10.1016/j.rser.2020.109725
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  doi: 10.1186/s13673-018-0136-7
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  doi: 10.1016/j.apenergy.2016.12.134
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  doi: 10.1016/j.egypro.2017.05.068
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  doi: 10.3390/s19112485
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  doi: 10.3390/en12050931
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  doi: 10.1109/MCOM.2017.1600267CM
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  doi: 10.1080/0951192X.2017.1314015
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  doi: 10.1016/j.knosys.2018.01.031
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  doi: 10.1039/C6JA00322B
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  doi: 10.1007/s12667-016-0203-y
– ident: ref25
  doi: 10.22214/ijraset.2017.4132
– ident: ref1
  doi: 10.30534/ijeter/2020/71892020
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– ident: ref16
  doi: 10.1016/j.patcog.2017.06.023
– ident: ref24
  doi: 10.1038/nrneurol.2016.187
– ident: ref9
  doi: 10.1016/j.enpol.2018.11.047
– ident: ref28
  doi: 10.1007/s00521-016-2645-5
– ident: ref7
  doi: 10.1016/j.energy.2018.07.084
– ident: ref19
  doi: 10.3389/fnins.2017.00324
– ident: ref31
  doi: 10.1109/TCSII.2019.2891704
– ident: ref5
  doi: 10.1186/s13673-018-0125-x
– ident: ref4
  doi: 10.1016/j.cities.2016.10.005
– ident: ref18
  doi: 10.1016/j.tics.2018.12.005
– ident: ref26
  doi: 10.1016/j.enbuild.2017.09.082
– ident: ref10
– ident: ref14
  doi: 10.1016/j.epsr.2016.08.031
– ident: ref33
  doi: 10.1109/ACCESS.2019.2939595
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