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| Main Authors: | , , , |
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| Formato: | Recurso digital |
| Idioma: | |
| Publicado: |
Zenodo
2026
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| Subjects: | |
| Acceso en liña: | https://doi.org/10.5281/zenodo.19640828 |
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Table of Contents:
- Abstract - Residential electricity management in India is hindered by the opacity of multi-slab tariff structures and the absence of accessible, predictive consumer tools. This paper presents an intelligent, full-stack web application — the Electricity Consumption Predictor — designed to forecast bi-monthly electricity usage and estimate TNEB-compliant utility bills for residential households in Tamil Nadu. The system integrates four principal modules: (1) a household appliance profiling interface capturing granular usage parameters; (2) an XGBoost regression engine trained on synthesized domestic consumption data to predict electricity units (kWh); (3) a deterministic TNEB tariff calculator mapping predicted units through the revised 2024 multi-slab pricing model; and (4) a dynamic budget analysis and personalised energy-insights dashboard. Built on a React.js frontend communicating with a FastAPI/Python backend via RESTful JSON endpoints, the platform delivers predictions with an RMSE of 8.1 units under baseline conditions and an R² score of up to 0.965 for typical residential profiles. Response latency remains below 160 ms under full computational load. Experimental evaluation across five household profiles and four seasonal conditions confirms that the system substantially outperforms generic online calculators and static TNEB portals in predictive granularity, tariff accuracy, and actionable user feedback. The open- architecture design supports future extension to IoT smart-meter integration, multi-regional tariff databases, and solar energy offset modelling.