Classification-Enhanced and hybrid forecasting paradigms in recent Short-Term electricity load Forecasting: A PRISMA-Based Systematic review

Accurate short-term electricity load forecasting (STLF) is essential for reliable power system operation, electricity market efficiency, demand-side management, and renewable energy integration. However, increasing distributed energy resources, electric vehicle adoption, smart-grid deployment, and changing consumer behavior have introduced nonlinear and heterogeneous load patterns that challenge conventional forecasting models. This study presents a PRISMA 2020-based systematic review of 74 Q1-ranked journal articles published between 2023 and 2025. The reviewed studies are categorized into statistical, machine learning, deep learning, and hybrid forecasting paradigms, with particular emphasis on distinguishing classification-enhanced pipelines from direct forecasting models. The synthesis shows that classification and clustering are frequently associated with improved robustness when load profiles are heterogeneous, event-driven, or behaviorally segmented, such as during holidays, weekends, regime shifts, or customer-group-specific demand patterns. However, their benefits are context-dependent and should not be interpreted as universal superiority. Direct forecasting models using decomposition, attention mechanisms, transfer learning, or Transformer-based architecture remain competitive under stable, aggregated, or well-preprocessed conditions. The review further highlights key limitations in the literature, including inconsistent validation protocols, limited risk-of-bias assessment, insufficient computational benchmarking, and underreported deployment feasibility. Overall, this study provides a structured synthesis of recent STLF research and identifies future directions toward interpretable, scalable, computationally feasible, and standardized forecasting frameworks.