A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916016397746176 |
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| author | Manjunath, Pavan Pruefer, Thomas |
| author_facet | Manjunath, Pavan Pruefer, Thomas |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16250 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation Manjunath, Pavan Pruefer, Thomas Computation and Language Artificial Intelligence Databases Machine Learning 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 |
| title | A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation |
| topic | Computation and Language Artificial Intelligence Databases Machine Learning |
| url | https://arxiv.org/abs/2605.16250 |