Abstract
Smart grid (SG) technologies have revolutionized energy resource management, with data preprocessing and feature engineering playing key roles in demand response strategies and green machine learning applications. Data preprocessing improves data quality and reliability by addressing issues such as incompleteness, noise, and inconsistencies, enabling informed decisions. Feature engineering extracts relevant information from raw data, capturing essential patterns and relationships, optimizing machine learning algorithms’ performance. These techniques enable demand response programs to predict and adapt to fluctuating energy demands in real-time, leading to efficient energy consumption management and reduced peak loads. Green machine learning uses these techniques to develop ecoconscious energy solutions, prioritizing low-carbon and renewable energy sources (RESs). By harnessing data-driven approaches, SGs can lead to a sustainable, energy-efficient future, fostering an environmentally conscious and resilient energy ecosystem.
Published Version
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