Towards Explainable and Resilience-Oriented Electricity-Demand Forecasting: A Structured Retrospective Narrative Systematic Review and Integrated Framework for AI-Enabled Energy Governance

Evidence on progress in AI-based electricity-demand forecasting is considerable but remains far from integrated, as it is spread across the domains of model accuracy, explainability and resilience. This structured retrospective narrative review synthesises the recoverable archived evidence corpus of a wider doctoral project, reconstructing and critically appraising the evidence without claiming full systematic-review reproducibility. A total of 759 DOI-level records were included in the historical search matrices, 236 remained after DOI/title normalisation, 109 underwent structured evidence extraction, and 31 were included in the focused synthesis and classified by the evidence role reported here. Because full database-specific search strings, exact search dates, and some historical screening details are non-recoverable, PRISMA 2020 is used as a transparency framework for the recoverable selection pathway rather than as a claim of full systematic-review compliance. The recoverable evidence was coded by forecasting architecture, data environment, forecasting horizon, validation metrics, explainability, uncertainty treatment, data-quality risks, and governance relevance. The comparative synthesis reveals that deep and hybrid models tend to perform better in data-rich predictive tasks, but the assessment of transferability, interpretability, uncertainty calibration, and robustness to data missingness, temporal misalignment, sensor anomalies, or distribution shift is inconsistent. The specific contribution of this paper is the operationalised Integrated Explainable-Resilient Forecasting Framework, which explicitly links multi-source data governance, hybrid forecasting, model explanation, stress testing, and decision support. Rather than focusing exclusively on accuracy, the framework evaluates forecast quality in terms of error, epistemic transparency, uncertainty calibration, resilience and institutional usability.