3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in energy-trading-4
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Use these libraries to find energy-trading-4 models and implementations
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A novel approach that makes use of independent learners Deep Q-learning algorithm to solve the problem of energy management in microgrid networks in the framework of stochastic games is proposed.
A peer-to-peer energy trading system among prosumers using a double auction-based game theoretic approach, where the buyer adjusts the amount of energy to buy according to varying electricity price in order to maximize benefit, to show the feasibility of real-time P2P trading.
The paper introduces an advanced Decentralized Energy Marketplace (DEM) integrating blockchain technol-ogy and artificial intelligence to manage energy exchanges among smart homes with energy storage systems. The proposed framework uses Non-Fungible Tokens (NFTs) to represent unique energy profiles in a transparent and secure trading environment. Leveraging Federated Deep Reinforce-ment Learning (FDRL), the system promotes collaborative and adaptive energy management strategies, maintaining user privacy. A notable innovation is the use of smart contracts, ensuring high efficiency and integrity in energy transactions. Extensive evaluations demonstrate the system's scalability and the effectiveness of the FDRL method in optimizing energy distribution. This research significantly contributes to developing sophisticated decentralized smart grid infras-tructures. Our approach broadens potential blockchain and AI applications in sustainable energy systems and addresses incentive alignment and transparency challenges in traditional energy trading mechanisms. The implementation of this paper is publicly accessible1,
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