Energy Adaptive Consensus Mechanism Using Renewable Energy Prediction for Sustainable Blockchain Networks

The exponential growth of blockchain technology has raised significant concerns about energy consumption and environmental sustainability. Traditional consensus mechanisms, particularly Proof-ofWork (PoW), consume substantial amounts of energy without considering the availability of renewable energy sources. This paper proposes an Energy-Adaptive Consensus Mechanism (EACM) that dynamically adjusts blockchain operations based on renewable energy prediction models. Our approach integrates machine learning-based forecasting of solar and wind energy availability with consensus timing and difficulty adjustments. Through comprehensive simulations and real-world data analysis, we demonstrate that EACM achieves a 35.2% reduction in carbon footprint while maintaining network security and decentralization properties. The proposed mechanism employs Long Short-Term Memory (LSTM) networks for renewable energy prediction and implements adaptive difficulty adjustment algorithms that optimize mining operations during periods of high renewable energy availability. Performance evaluation shows improved energy efficiency with minimal impact on transaction throughput and network latency. Our contributions include a novel consensus framework, predictive energy models, and empirical validation demonstrating the feasibility of sustainable blockchain operations.