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A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data.

Jenniffer S Guerrero-Prado1, Wilfredo Alfonso-Morales1, Eduardo F Caicedo-Bravo1

  • 1School of Electrical and Electronics Engineering, Faculty of Engineering, Universidad del Valle, Calle 13 #100-00, Cali P.O. Box 25360, Colombia.

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Summary

This study introduces a Data Analytics/Big Data framework for Advanced Metering Infrastructure (AMI) data, enhancing smart city applications. The framework leverages human expertise for optimal decision-making in energy markets, achieving load forecasting accuracy below 5% MAPE.

Keywords:
AMI dataSGAMadvanced metering infrastructurebig datadata analyticssmart citiessmart gridssmart meter

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Area of Science:

  • Data Science and Analytics
  • Smart Grids and Energy Systems
  • Urban Dynamics

Background:

  • Advanced Metering Infrastructure (AMI) data offers real-time insights beyond electricity consumption, reflecting social, demographic, and economic city dynamics.
  • Smart cities require robust frameworks to harness the full potential of diverse data sources like AMI.
  • Existing energy market challenges necessitate innovative approaches for efficient and optimal decision-making.

Purpose of the Study:

  • To present a Data Analytics/Big Data framework for leveraging AMI data within Smart City applications.
  • To integrate architectural and methodological views of data processing, emphasizing human expertise as a crucial component.
  • To demonstrate the framework's utility in addressing energy market challenges through improved decision-making.

Main Methods:

  • The framework integrates an architectural view (Smart Grids Architecture Model-SGAM) and a methodological view (DIKW hierarchy, NIST Big Data interoperability model).
  • A 'binding element' combining human expertise with data-derived knowledge is introduced to bridge the gap between data and wisdom.
  • A case study involving load forecasting for a Colombian Retail Electricity Provider (REP) was conducted to validate the framework.

Main Results:

  • The framework successfully transforms raw AMI data into actionable knowledge and wisdom.
  • The load forecasting application achieved a Mean Absolute Percentage Error (MAPE) of less than 5% in certain markets.
  • The 'binding element' demonstrated its value in generating new development alternatives and enabling feedback for decision-making.

Conclusions:

  • The proposed Data Analytics/Big Data framework effectively utilizes AMI data for smart city applications and energy market challenges.
  • Human expertise is essential for transforming data-driven knowledge into wisdom, supporting optimal and efficient decision-making.
  • The framework provides a valuable tool for enhancing energy management and urban planning through data insights.