Research & Development

Energy Transition Modeling: Short-Term Electricity Demand Forecasting Using Seq2Seq Encoder–Decoder Model

Book: Materials, Devices and Systems for Sustainability, IITK Directions, Volume 8

This paper introduces a Seq2Seq LSTM-based approach for short-term electricity demand forecasting that captures complex temporal and weather-driven patterns. It demonstrates improved forecasting performance compared to conventional methods, offering a scalable framework for energy prediction. This work informs our AI-native forecasting systems, enabling more accurate emissions projections and better planning under dynamic operational conditions.

A Smartphone-Based Hybrid Model for Real-Time Monitoring of Aggressive Driving Behavior

This paper highlights the core technical competency of CarbonTatva AI by demonstrating our ability to process complex, high-frequency sensor data (like telemetry) into actionable insights using advanced hybrid modeling. By combining Dynamic Time Warping (DTW) for temporal pattern recognition with Random Forest for statistical classification, we showcase a sophisticated approach to time-series analysis that is directly transferable to carbon monitoring. Specifically, this expertise allows us to precisely align and analyze fluctuating emission patterns across various industrial processes, ensuring that our sustainability dashboards provide a high level of accuracy in detecting anomalies and forecasting environmental impact as seen in driver behavior prediction.

Climate Change: Impact of Global Warming on India's Electricity Consumption

This study analyses how rising temperatures influence electricity demand across India, using state-level consumption and weather data. It highlights how temperature variations significantly impact demand patterns, with big regional and socio-economic differences. The findings provide a foundation for understanding climate-driven demand shifts, directly informing how we model emissions sensitivity and forecast future energy needs.