A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation
Pavan Manjunath, Thomas Pruefer
The paper describes an end-to-end system for utility billing that combines four components: a generative AI agent producing natural-language bill explanations from structured meter data using constrained decoding, a transformer-based day-ahead load forecaster with calibrated quantile uncertainty bands, a carbon intensity attribution module that assigns a defensible CO₂ number to each kWh consumed, and a demand-response scheduler that optimizes load against grid stress and emissions signals. No quantitative benchmarks or ablation results are visible in the available abstract, so the claimed performance advantages over existing billing or forecasting systems cannot be independently assessed.
No production traction yet. All GitHub references are arXiv digest and RSS aggregator repos, not implementations. Zero citations on Semantic Scholar. This reads as a position/architecture paper pitched at utility operators, but there is no code, no dataset, and no third-party validation attached to it.
Distribution utilities are now expected to deliver bills that customers can actually read attach a defensible carbon number to every kWh sold and schedule load against grid stress and emissions constraints We propose an end-to-end framework that unifies four production-grade capabilities under one architectural roof a generative-AI agent that drafts each customers natural-language billing statement from structured numeric inputs under a constrained decoding policy a transformer-based forecaster that supplies the day-ahead consumption estimate with calibrated quantile bands