DATA SCIENCE IN ENERGY CONSUMPTION ANALYSIS: A REVIEW OF AI TECHNIQUES IN IDENTIFYING PATTERNS AND EFFICIENCY OPPORTUNITIES

Abstract

This review critically examines the role of Data Science and Artificial Intelligence (AI) techniques in energy consumption analysis, focusing on their efficacy in identifying patterns and uncovering efficiency opportunities. The primary objective is to assess how AI methodologies are transforming energy consumption analysis, with an emphasis on pattern recognition and optimization of energy efficiency. The study adopts a systematic literature review approach, scrutinizing peer-reviewed articles published between 2015 and 2022. This methodological framework ensures a comprehensive and relevant analysis of current AI applications in the energy sector. Key findings reveal a significant evolution from traditional energy analysis methods to sophisticated AI-driven techniques. AI has proven instrumental in accurately predicting energy consumption patterns, facilitating enhanced decision-making for energy management. The review identifies various AI techniques, including machine learning, deep learning, and predictive analytics, and their specific applications in energy consumption analysis. The study also delves into the technological, economic, and environmental implications of integrating AI in energy analysis, highlighting both the challenges and potential solutions. It underscores the growing trend of AI applications in enhancing energy efficiency and the emerging opportunities therein. This offers a comprehensive overview of current trends and future directions, serving as a guide for industry stakeholders, policymakers, and researchers in harnessing AI for more efficient and sustainable energy consumption analysis. Keywords: Artificial Intelligence, Efficiency Optimization, Pattern Recognition, Energy Consumption Analysis.