Integrating Smart Contracts and Forecasting Models for Sustainable Energy Grids in India

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:The hybrid LSTM-Random Forest model integrated with smart contracts offers a novel approach for real-time energy trading optimization in distributed grids.

🏢実務担当者:The platform's demonstrated 35-45% cost savings and automated carbon credit accounting provide a blueprint for corporate sustainability teams in energy-intensive industries.

🏛政策担当者:The paper's discussion of state-level P2P trading guidelines (Karnataka) and alignment with Digital India offers insights for regulatory sandboxes and grid modernization policies.

India's ambitious renewable energy targets of 500 GW by 2030 and net-zero emissions by 2070 necessitate transformative energy trading solutions capable of harnessing distributed renewable sources. This paper introduces a blockchain-enabled peer-to-peer (P2P) energy trading platform designed for India's diverse energy landscape, which includes rooftop solar, wind plants, and microgrids in both urban and rural areas. Built on the Ethereum foundation, the platform employs smart contracts to automate energy transactions between prosumers, reducing dependence on the conventional grid and advancing India's energy security goals. The system integrates machine learning algorithms trained on specific Indian usage patterns and weather conditions to forecast optimal trading times, accounting for seasonal changes, festivals, and industrial demand cycles. Key model assumptions include: (i) prosumers have bidirectional smart meters with IoT connectivity; (ii) weather data availability from Indian Meteorological Department stations; (iii) baseline electricity tariffs following state-level regulatory frameworks; and (iv) participants operate within Karnataka Electricity Regulatory Commission's P2P trading guidelines. Core parameters include LSTM networks with 50 hidden units, learning rate of 0.001, and 24-hour prediction horizons; Random Forest models with 100 estimators and maximum depth of 10; smart contract gas limits of $3,000,000$ units; and dynamic pricing coefficients $\alpha=0.15$ and $\beta=0.08$ calibrated against Tamil Nadu industrial tariffs. Through automated transactions, the platform allows small-scale generators to sell surplus energy directly to local consumers, mitigating the $18-20 \%$ distribution losses typical of the Indian grid. Pilot studies in Tamil Nadu and Maharashtra showcased significant results, including a 35-45% cost saving for participating industries and transparent carbon credit accounting, aligning with emerging ESG compliance needs. The platform contributes to the Digital India initiative by fostering a decentralized energy infrastructure that supports both economic development and environmental sustainability.

• semanticscholar https://doi.org/10.1109/odicon66687.2026.11470831first seen 2026-05-15 17:28:26