Electricity theft remains a critical threat to power distribution infrastructure globally, with annual losses exceeding USD 89 billion and non-technical loss rates reaching 40% in developing economies. While machine learning has emerged as the dominant analytical approach for automated theft detection in smart grid environments, the field lacks a unifying framework that connects algorithm selection to the operational realities of Distribution System Operators (DSOs). Existing reviews catalogue methods and report benchmark metrics without addressing how detection paradigm selection should be aligned with data maturity, regulatory requirements, computational constraints, and institutional capacity. This review addresses that gap by systematically analysing 90 peer-reviewed studies published between 2015 and 2025, identified through structured multi-database searches, screened against explicit eligibility criteria, and graded with a formal five-criterion quality rubric, through a unified adversarial time-series formulation that provides a consistent analytical lens across all major learning paradigms. The analysis covers supervised ensemble methods, unsupervised and semi-supervised anomaly detection, deep learning architectures, including convolutional neural networks, long short-term memory networks and Transformer models, graph neural networks, federated learning, and explainable artificial intelligence. Key findings reveal that no single paradigm achieves optimality across all deployment dimensions simultaneously, that gradient boosting methods deliver near state-of-the-art performance with significantly lower computational overhead than deep learning, and that hybrid architectures achieve AUC-ROC scores of 0.95 to 0.98 on benchmark datasets but require complementary governance mechanisms to satisfy regulatory defensibility requirements. A lifecycle-aligned deployment framework and a layered detection architecture are proposed, offering practitioners a structured pathway from early AMI rollout through to advanced smart grid deployment. The principal outcomes of the review are a formal characterisation of which component of the detection problem each learning paradigm estimates, quality-graded and harmonised benchmark performance ranges, and a quantified illustrative analysis indicating that the proposed layered architecture can improve inspection productivity by roughly an order of magnitude at a fixed field budget. Four priority research challenges are identified: real-time edge detection, continual learning, multi-modal data fusion, and standardised benchmarking.
Oluwagbenga Apata, Mukovhe Ratshitanga, I. Davidson· Energies· 0 citations
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.
Oluwagbenga Apata, Mukovhe Ratshitanga, I. Davidson· Energies· 0 citations
Accurate short-term load forecasting (STLF) is essential for modern grid operations, enabling efficient scheduling, demand response, and renewable energy integration. This paper presents a systematic comparison of five forecasting architectures applied to a large dataset of 98 residential homes, with 1-minute and 15-minute smart meter readings spanning 2015-2023. The models include an XGBoost pipeline with extensive feature engineering, a tuned CatBoost implementation, a feedforward neural network with multi-output regression, a multi-scale convolutional Kolmogorov-Arnold network (MCKAN), and a long short-term memory (LSTM) network with a 7-day lookback. All models are trained globally, pooling data across homes while incorporating home-specific categorical variables, including an assignment to a simulated microgrid topology with nine kiosks and three phases. Hyperparameter optimization is performed using Optuna and Keras Tuner. CatBoost achieves the lowest test MAE across all horizons, from 0.4089 (15-minute) to 0.7419 (30-day), outperforming XGBoost by 4-10% and deep learning models by larger margins. The findings support Sustainable Development Goals 7, 9, and 13 and provide actionable insights for energy management, particularly in the South African context of load shedding and grid decarbonization.
Mukovhe Ratshitanga, Pfano Nemakonde, Komla A. Folly et al.· 2026 6th International Confe...· 0 citations
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