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BIG DATA PROCESSING AND CORRELATION ANALYSIS OF ELECTRIC POWER MARKETING BASED ON IMPROVED APRIORI ALGORITHM AND RDD MODEL.

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Title: BIG DATA PROCESSING AND CORRELATION ANALYSIS OF ELECTRIC POWER MARKETING BASED ON IMPROVED APRIORI ALGORITHM AND RDD MODEL.
Authors: Pan, Fan, Zhou, Lingen, Gan, Lu, Kang, Wei, Li, Xiaolei
Source: Archives for Technical Sciences / Arhiv za Tehnicke Nauke; 2025, Issue 34, p1379-1397, 19p
Subject Terms: Apriori algorithm, Distributed computing, Statistical correlation, Parallel processing, Electronic data processing, Big data, Association rule mining, Electricity markets
Abstract: To solve the problems of traditional Apriori algorithm in power marketing big data processing, such as candidate item set redundancy, low single-machine computing efficiency, and difficulty in adapting to multi-dimensional time series data, this study proposes an improved Apriori algorithm that integrates Resilient Distributed Dataset (RDD) distributed architecture. This study takes two public data sets as the research object. It first uses RDD distributed architecture to complete data cleaning, missing value filling, outlier elimination and feature conversion. Then, it optimizes the pruning strategy and parallel support statistical method to address the shortcomings of insufficient pruning and redundant support calculation of traditional algorithms. The experimental results show that when the improved algorithm processes 1 million pieces of electricity marketing data, the running time is reduced from 486.5s to 183.4s compared to native Apriori. When processing 5 million pieces of real electricity marketing data, the speedup ratio of the improved algorithm reaches 3.75 at five nodes, and the expansion rate remains at 79%. A total of 12 core association rules for power marketing were discovered. Among them, typical rules such as "industrial users → high load from 9:00 to 18:00 on weekdays" and "high temperature >35°C+residential users → surge in air conditioning load" have an average support degree of 0.71, an average confidence level of 0.83, and an improvement degree greater than 1.2. The research conclusion confirms that the integration solution of the improved algorithm and RDD model can efficiently process power marketing big data, and the mined association rules have actual business value. This research provides data support and technical reference for power companies to formulate peak-shifting electricity price policies, optimize regional power supply planning, and provide precise marketing services. This is of great significance in promoting the transformation of electric power marketing to intelligence and refinement. [ABSTRACT FROM AUTHOR]
Copyright of Archives for Technical Sciences / Arhiv za Tehnicke Nauke is the property of Archives for Technical Science / Arhiv za tehnicke nauke and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: BIG DATA PROCESSING AND CORRELATION ANALYSIS OF ELECTRIC POWER MARKETING BASED ON IMPROVED APRIORI ALGORITHM AND RDD MODEL.
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  Data: <searchLink fieldCode="AR" term="%22Pan%2C+Fan%22">Pan, Fan</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Lingen%22">Zhou, Lingen</searchLink><br /><searchLink fieldCode="AR" term="%22Gan%2C+Lu%22">Gan, Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Kang%2C+Wei%22">Kang, Wei</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Xiaolei%22">Li, Xiaolei</searchLink>
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  Data: Archives for Technical Sciences / Arhiv za Tehnicke Nauke; 2025, Issue 34, p1379-1397, 19p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Apriori+algorithm%22">Apriori algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22Electricity+markets%22">Electricity markets</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To solve the problems of traditional Apriori algorithm in power marketing big data processing, such as candidate item set redundancy, low single-machine computing efficiency, and difficulty in adapting to multi-dimensional time series data, this study proposes an improved Apriori algorithm that integrates Resilient Distributed Dataset (RDD) distributed architecture. This study takes two public data sets as the research object. It first uses RDD distributed architecture to complete data cleaning, missing value filling, outlier elimination and feature conversion. Then, it optimizes the pruning strategy and parallel support statistical method to address the shortcomings of insufficient pruning and redundant support calculation of traditional algorithms. The experimental results show that when the improved algorithm processes 1 million pieces of electricity marketing data, the running time is reduced from 486.5s to 183.4s compared to native Apriori. When processing 5 million pieces of real electricity marketing data, the speedup ratio of the improved algorithm reaches 3.75 at five nodes, and the expansion rate remains at 79%. A total of 12 core association rules for power marketing were discovered. Among them, typical rules such as "industrial users → high load from 9:00 to 18:00 on weekdays" and "high temperature >35°C+residential users → surge in air conditioning load" have an average support degree of 0.71, an average confidence level of 0.83, and an improvement degree greater than 1.2. The research conclusion confirms that the integration solution of the improved algorithm and RDD model can efficiently process power marketing big data, and the mined association rules have actual business value. This research provides data support and technical reference for power companies to formulate peak-shifting electricity price policies, optimize regional power supply planning, and provide precise marketing services. This is of great significance in promoting the transformation of electric power marketing to intelligence and refinement. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Archives for Technical Sciences / Arhiv za Tehnicke Nauke is the property of Archives for Technical Science / Arhiv za tehnicke nauke and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.70102/afts.2025.1834.1379
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 1379
    Subjects:
      – SubjectFull: Apriori algorithm
        Type: general
      – SubjectFull: Distributed computing
        Type: general
      – SubjectFull: Statistical correlation
        Type: general
      – SubjectFull: Parallel processing
        Type: general
      – SubjectFull: Electronic data processing
        Type: general
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Association rule mining
        Type: general
      – SubjectFull: Electricity markets
        Type: general
    Titles:
      – TitleFull: BIG DATA PROCESSING AND CORRELATION ANALYSIS OF ELECTRIC POWER MARKETING BASED ON IMPROVED APRIORI ALGORITHM AND RDD MODEL.
        Type: main
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            NameFull: Pan, Fan
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            NameFull: Zhou, Lingen
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            NameFull: Gan, Lu
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            NameFull: Kang, Wei
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            NameFull: Li, Xiaolei
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            – D: 01
              M: 12
              Text: 2025
              Type: published
              Y: 2025
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              Value: 34
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            – TitleFull: Archives for Technical Sciences / Arhiv za Tehnicke Nauke
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