AI-Driven Load Forecasting for Dynamic Tariff Structuring: A Comprehensive Review

Abstract

Dynamic electricity tariffs are increasingly deployed to manage demand-side flexibility in decarbonising power systems, making AI-driven load forecasting a critical enabler of adaptive pricing. However, existing studies largely treat forecasting and tariff design as independent problems, evaluating models on predictive accuracy alone while neglecting the feedback effects through which price signals reshape consumer behaviour and introduce non-stationarity into the very demand distributions forecasts depend upon. This gap leaves practitioners without coherent guidance on how to structure, govern, and adapt forecasting models in price-responsive environments. This paper addresses the gap through a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-informed structured review of peer-reviewed studies spanning statistical, machine learning, deep learning, probabilistic, and reinforcement learning forecasting paradigms. Three principal findings emerge. First, dynamic pricing fundamentally invalidates static forecasting assumptions by inducing a distributional shift in demand data, making robustness to behavioural feedback a first-order requirement for deployment. Second, no single forecasting paradigm simultaneously satisfies the requirements of accuracy, interpretability, uncertainty quantification, and regulatory defensibility that dynamic tariff systems impose; layered, role-specific architectures are therefore operationally necessary. Third, explainability and governance constraints are structural requirements for tariff-oriented forecasting, not optional enhancements, because forecast outputs directly influence economically and socially consequential pricing decisions. Building on these findings, the paper proposes a Forecast-Driven Dynamic Tariff Design Framework that separates forecasting intelligence from pricing authority, embeds uncertainty management as a first-class design element, and positions a governance layer as the mandatory interface between predictive outputs and consumer-facing tariff signals. The framework provides a practical and regulatorily defensible foundation for deploying adaptive, resilient, and equitable electricity tariffs in data-intensive power systems.