AI-driven household electricity load forecasting: challenges, methods, and future directions

Abstract Accurate household electricity load forecasting is becoming increasingly vital with the continued growth of smart grids, household renewable energy systems, and smart meter deployment. Unlike regional or grid load forecasting, household-level forecasting presents unique challenges due to highly irregular consumption patterns, data scarcity, privacy concerns, and behavioral variability. In recent years, artificial intelligence (AI) methods have demonstrated strong potential to address these complexities, enabling more accurate, robust, and adaptive forecasting systems. This survey presents a comprehensive and up-to-date review, with a focus on AI-based techniques specifically tailored to household-level forecasting. A key contribution of this work is the development of a challenge-centered taxonomy that categorizes methods based on four critical problem domains: methodological limitations, data-related constraints, behavioral complexity, and privacy and security concerns. By aligning representative AI approaches with these core challenges, the survey offers a structured and insightful understanding of the current research landscape. It also provides a comparative analysis with prior surveys, identifies gaps in the literature, and highlights promising research directions, including multimodal learning, adaptive modeling, integration of large language models, and privacy-preserving forecasting. This work could serve as a valuable resource for researchers and practitioners aiming to advance intelligent and trustworthy forecasting solutions in household energy systems.