首页|Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

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Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems.Existing methods primarily focus on data management,rather than emphasizing efficiency.Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demand-supply balancing.Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs.Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns.The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization.We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels.Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters.The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE).The proposed model demonstrates superior performance,achieving a maximum accuracy of 65.917％and a minimum MSE of 0.096.These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

electricity consumptionpredictive modeldata analyticssmart meteringmachine learning

K.Rama Devi、V.Srinivasan、G.Clara Barathi Priyadharshini、J.Gokulapriya

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Department of Information Technology,Panimalar Engineering College,Chennai,Tamil Nadu 600029,India

Department of Computer Applications,Dayananda Sagar College of Engineering,Bengaluru 560078,Karnataka,India

Department of Computer Appliations,Karpagam Academy of Higher Education,Coimbatore,Tamil Nadu 641021,India

Department of Computer Science,Rathinam College of Arts and Science,Coimbatore,Tamil Nadu 641021,India

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2024

10.11916/j.issn.1005-9113.2023014

哈尔滨工业大学学报(英文版)
哈尔滨工业大学

哈尔滨工业大学学报(英文版)

影响因子：0.238
ISSN：1005-9113
年,卷(期)：2024.31(1)
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