Verification and Validation of Machine Learning Outputs in Regulated Energy and Emissions Reporting Workflows

Abstract

The increasing application of machine learning in energy and emissions reporting has posed grave questions on the aspects of reproducibility, explainability, defensibility of uncertainty, and regulatory admissibility. Conventional machine learning systems primarily aim at predictive performance, and controlled reporting systems require predictable, verifiable, and auditable results to conform to the legal and operational constraints. The paper introduces a verification and validation system of machine learning outputs based on assurance in regulated energy and emissions reporting systems. The framework breaks down the working process of operation into particular training, inference, verification and reporting sectors to make sure that uncontrolled analytical behaviour does not infiltrate to the regulator confronted with disclosures. The proposed model integrates the use of XGBoost regression to predict emissions, Isolation Forest to identify anomalies, conformal prediction to confirm the uncertainty, and explainability analysis with SHAP. Reproducibility and regulatory robustness were ensured by verification invariants, deterministic execution environments, evidence preservation controls for governance, and controlled reporting interfaces. These mechanisms were evaluated using 1.2 million industrial simulated reporting instances collected from 12 facilities over 24 reporting periods. The outcome of experiments showed high predictive and operational performance. The XGBoost model achieved an