Machine Learning Techniques in Residential Electrical Load Forecasting: A PRISMA Review with LLM-Assisted Screening and Evidence Extraction - Siphocly, Nermin (European Scientific Journal, ESJ, 2026)
This systematic review assesses machine learning (ML) techniques for residential electrical load forecasting, highlighting their effectiveness, methodologies, and challenges. Conducted under PRISMA 2020 guidelines, the review includes peer-reviewed Q1 and Q2 journal studies published between January 2020 and June 2025. These studies, sourced from the Web of Science, were selected based on their use of ML methods in residential contexts and focus on forecasting performance metrics and implementation. Extracted data covered forecasting horizons, ML algorithms, performance metrics, and limitations. Database searching yielded 712 records. After refinement and eligibility screening, 214 records were retained for title and abstract review, of which 105 were excluded. Only 93 full-text articles could be retrieved and assessed, seven of which were ultimately excluded due to methodological or contextual ineligibility, leaving the final 86 eligible studies. We present a novel multi-stage screening pipeline that incorporates semantic similarity models—specifically a zero-shot retrieval hybrid classifier—and large language models (ChatGPT-4o and Grok 3). Additionally, we examine their behavior, performance, and misclassification patterns throughout the screening process. We highlight key gaps in current literature-reproducibility issues, geographical imbalance, LLM bias, and limited use of explainable or privacy-preserving models—and suggest future research directions for residential load forecasting.