View in EDS

IoT‐Enabled Smart Metering With Transfer Learning for Optimized Energy Load Forecasting.

Saved in:
Bibliographic Details
Title: IoT‐Enabled Smart Metering With Transfer Learning for Optimized Energy Load Forecasting.
Authors: Ahmed, Muhammad Maroof1 (AUTHOR), Ditta, Allah2 (AUTHOR), Siddiqa, Ayesha3 (AUTHOR), Wazir, Perwasha4 (AUTHOR), Shah, Asghar Ali5 (AUTHOR) asghar.ali.shah@kateb.edu.af, Adnan, Khan Muhammad6 (AUTHOR) adnan@gachon.ac.kr, Du, Cheng (AUTHOR) cdu@wiley.com
Source: International Journal of Energy Research. 7/8/2026, Vol. 2026, p1-13. 13p.
Subjects: Smart meters, Energy consumption forecasting, Real-time computing, Energy consumption, Energy management, Machine learning, Internet of things
Abstract: Rapid urbanization and increasing electricity demand require an enhanced energy management system (EMS) to ensure better use of limited energy resources. Smart energy metering systems play a vital role in monitoring power consumption and forecasting future energy demand. Weather conditions, including temperature, humidity, and wind speed, significantly influence energy consumption patterns by affecting heating, cooling, ventilation, and appliance usage. This study aims to develop an IoT‐enabled smart energy meter integrated with a transfer learning (TL)‐based forecasting model using real‐time electrical and weather parameters, including voltage, current, power, frequency, power factor, energy consumption, temperature, humidity, wind speed, and atmospheric pressure. A real‐time dataset was collected over 3 months and analyzed using random forest (RF), support vector machine (SVM), artificial neural network (ANN), long short‐term memory (LSTM), and recurrent neural network (RNN) models, where the RNN achieved the best forecasting performance with minimum prediction errors. System performance was evaluated, including mean absolute error (MAE), mean square error (MSE), root MSE (RMSE), and mean absolute percentage error (MAPE). Experimental results demonstrate the effectiveness of the proposed method, achieving 2.18%, 5.45%, 2.34%, 14.43%, and an accuracy performance (AP) of 85%. These findings highlight the system's accuracy and its potential to significantly enhance energy management, reduce unnecessary consumption, and provide real‐time insights to consumers. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Energy Research is the property of Wiley-Blackwell 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.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 195176722
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: IoT‐Enabled Smart Metering With Transfer Learning for Optimized Energy Load Forecasting.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ahmed%2C+Muhammad+Maroof%22">Ahmed, Muhammad Maroof</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ditta%2C+Allah%22">Ditta, Allah</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Siddiqa%2C+Ayesha%22">Siddiqa, Ayesha</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wazir%2C+Perwasha%22">Wazir, Perwasha</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shah%2C+Asghar+Ali%22">Shah, Asghar Ali</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> asghar.ali.shah@kateb.edu.af</i><br /><searchLink fieldCode="AR" term="%22Adnan%2C+Khan+Muhammad%22">Adnan, Khan Muhammad</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> adnan@gachon.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Du%2C+Cheng%22">Du, Cheng</searchLink> (AUTHOR)<i> cdu@wiley.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Energy+Research%22">International Journal of Energy Research</searchLink>. 7/8/2026, Vol. 2026, p1-13. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Smart+meters%22">Smart meters</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption+forecasting%22">Energy consumption forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+management%22">Energy management</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Rapid urbanization and increasing electricity demand require an enhanced energy management system (EMS) to ensure better use of limited energy resources. Smart energy metering systems play a vital role in monitoring power consumption and forecasting future energy demand. Weather conditions, including temperature, humidity, and wind speed, significantly influence energy consumption patterns by affecting heating, cooling, ventilation, and appliance usage. This study aims to develop an IoT‐enabled smart energy meter integrated with a transfer learning (TL)‐based forecasting model using real‐time electrical and weather parameters, including voltage, current, power, frequency, power factor, energy consumption, temperature, humidity, wind speed, and atmospheric pressure. A real‐time dataset was collected over 3 months and analyzed using random forest (RF), support vector machine (SVM), artificial neural network (ANN), long short‐term memory (LSTM), and recurrent neural network (RNN) models, where the RNN achieved the best forecasting performance with minimum prediction errors. System performance was evaluated, including mean absolute error (MAE), mean square error (MSE), root MSE (RMSE), and mean absolute percentage error (MAPE). Experimental results demonstrate the effectiveness of the proposed method, achieving 2.18%, 5.45%, 2.34%, 14.43%, and an accuracy performance (AP) of 85%. These findings highlight the system's accuracy and its potential to significantly enhance energy management, reduce unnecessary consumption, and provide real‐time insights to consumers. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Energy Research is the property of Wiley-Blackwell 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195176722
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1155/er/1719445
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 1
    Subjects:
      – SubjectFull: Smart meters
        Type: general
      – SubjectFull: Energy consumption forecasting
        Type: general
      – SubjectFull: Real-time computing
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Energy management
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Internet of things
        Type: general
    Titles:
      – TitleFull: IoT‐Enabled Smart Metering With Transfer Learning for Optimized Energy Load Forecasting.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ahmed, Muhammad Maroof
      – PersonEntity:
          Name:
            NameFull: Ditta, Allah
      – PersonEntity:
          Name:
            NameFull: Siddiqa, Ayesha
      – PersonEntity:
          Name:
            NameFull: Wazir, Perwasha
      – PersonEntity:
          Name:
            NameFull: Shah, Asghar Ali
      – PersonEntity:
          Name:
            NameFull: Adnan, Khan Muhammad
      – PersonEntity:
          Name:
            NameFull: Du, Cheng
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 08
              M: 07
              Text: 7/8/2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0363907X
          Numbering:
            – Type: volume
              Value: 2026
          Titles:
            – TitleFull: International Journal of Energy Research
              Type: main
ResultId 1