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.
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