Decentralized digital intelligence: IoT-ML for Indian healthcare renewable energy consumption and forecasting – A complete review with prospects and challenges
Year: 2026
Type: article
Abstract: A comprehensive review of one year that investigates the revolutionary possibilities of integrating IoT (Internet of Things) and ML (Machine Learning) for renewable energy forecasting in Indian healthcare systems, with a focus on the rural village of Vellore, Tamil Nadu. Increased patient mortality due to unpredictable power supplies from various regions around the world. Over 300 articles indexed by SCI (Science Citation Index) from 2014 to 2025 and real case studies address power outages using IoT for real-time monitoring (solar irradiance: 1000 W/m², wind speed: 5–7 m/s) and ML models (LSTM (Long Short Term Memory), CNN-LSTM (Convolutional Neural Network-Long Short Term Memory), XG Boost (Extreme Gradient), RF (Random Forest), ANN (Artificial Neural Network) for forecasting on weekly, monthly, and yearly timescales, expanding on previous research's short and medium term emphasis. A 100-kW hybrid solar-wind system (20 kW PV, 10 kW wind) with edge-cloud optimizes demand (4000–4250 kWh/day for 200 beds) and reduces peak load (200–210 kW) by 15–20%, achieving 85–95% accuracy and 15–35% efficiency. Case studies demonstrate a 5–7-year ROI (Return on Investment), 15–40% CO₂ reduction, and 95% uptime (>99.9% with IoT). NB (Narrow band)-IoT/5G microgrids, subsidies (0.05–0.07 $/kWh), and IoT-ML to raise accuracy by 10–20% are proposed to improve healthcare resilience and global sustainability. This pioneering study lays the groundwork for hospital renewable energy infrastructure assessments.
Source: Energy Reports
Authors: P. Renugadevi, Sudha Anbalagan, R. Srimathi
Institution: Vellore Institute of Technology University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,040
OpenAlex ID: W7141301423