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A Comprehensive Data Science Framework for Electricity Distribution Analysis: Integrating Machine Learning, Ethical Considerations, and Crisp-Dm Methodology

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A Comprehensive Data Science Framework for Electricity Distribution Analysis: Integrating Machine Learning, Ethical Considerations, and Crisp-Dm Methodology


Chinonso Job | Onwe, Festus Chijioke



Chinonso Job | Onwe, Festus Chijioke "A Comprehensive Data Science Framework for Electricity Distribution Analysis: Integrating Machine Learning, Ethical Considerations, and Crisp-Dm Methodology" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-10 | Issue-2, April 2026, pp.427-434, URL: https://www.ijtsrd.com/papers/ijtsrd106994.pdf

The increasing demand for electricity across all sectors of human activity necessitates sophisticated analytical approaches for optimizing distribution systems while ensuring data privacy and ethical compliance. This study presents a comprehensive framework for analyzing electricity distribution data using data science methodologies, with particular emphasis on the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Utilizing electricity distribution data from the National Bureau of Statistics (NBS) spanning 2015 to Q2 2024, we evaluate multiple data science tools including R, Python, and TensorFlow, alongside various analytical approaches encompassing machine learning, deep learning, statistical analysis, and exploratory data analysis. The study systematically compares four data science management approaches-CRISP-DM, Agile, Team Data Science Process (TDSP), and SEMMA-providing evidence-based recommendations for electricity distribution organizations. Furthermore, we conduct a critical examination of ethical challenges inherent in electricity data analysis, focusing on bias mitigation, privacy preservation, and transparency requirements. Our findings indicate that the combination of R programming for statistical analysis, machine learning approaches for pattern recognition and forecasting, and CRISP-DM methodology for project management offers the most robust framework for electricity distribution data analysis. The study contributes practical guidelines for utility companies seeking to leverage data science while maintaining ethical standards and regulatory compliance, ultimately supporting improved decision-making in the energy sector.

Electricity Distribution, Data Science, Machine Learning, Deep Learning, CRISP-DM, Data Mining, Privacy, Bias Mitigation, Transparency, Smart Grid, Energy Analytics.


IJTSRD106994
Volume-10 | Issue-2, April 2026
427-434
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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