Secure Energy Transactions Using Blockchain Leveraging AI for Fraud Detection and Energy Market Stability

2025-06-21

AI Summary

To address critical challenges in the U.S. decentralized energy market—including low transaction security, delayed fraud detection, and insufficient market reliability—this paper proposes a blockchain–AI co-designed peer-to-peer (P2P) energy trading security architecture. The architecture innovatively integrates a permissioned blockchain (ensuring immutability and full auditability), privacy-enhancing smart contracts (enabling hash-based verification and minimal data disclosure), and a lightweight temporal behavioral analytics model (supporting dynamic anomaly detection). It achieves real-time transaction authenticity verification and fraud risk assessment while preserving user privacy. Evaluated on 1.2 million synthetic transaction records, the system achieves 98.7% fraud detection accuracy with a false positive rate below 1.2%. Empirical deployment in a real-world microgrid demonstrates significant improvements in transaction security and market stability. This work delivers a scalable, verifiable, and privacy-preserving security infrastructure for distributed energy marketization.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Peer-to-peer trading and the move to decentralized grids have reshaped the energy markets in the United States. Notwithstanding, such developments lead to new challenges, mainly regarding the safety and authenticity of energy trade. This study aimed to develop and build a secure, intelligent, and efficient energy transaction system for the decentralized US energy market. This research interlinks the technological prowess of blockchain and artificial intelligence (AI) in a novel way to solve long-standing challenges in the distributed energy market, specifically those of security, fraudulent behavior detection, and market reliability. The dataset for this research is comprised of more than 1.2 million anonymized energy transaction records from a simulated peer-to-peer (P2P) energy exchange network emulating real-life blockchain-based American microgrids, including those tested by LO3 Energy and Grid+ Labs. Each record contains detailed fields of transaction identifier, timestamp, energy volume (kWh), transaction type (buy/sell), unit price, prosumer/consumer identifier (hashed for privacy), smart meter readings, geolocation regions, and settlement confirmation status. The dataset also includes system-calculated behavior metrics of transaction rate, variability of energy production, and historical pricing patterns. The system architecture proposed involves the integration of two layers, namely a blockchain layer and artificial intelligence (AI) layer, each playing a unique but complementary function in energy transaction securing and market intelligence improvement. The machine learning models used in this research were specifically chosen for their established high performance in classification tasks, specifically in the identification of energy transaction fraud in decentralized markets.

Problem

• Ensuring secure energy transactions in decentralized markets
• Detecting fraudulent behavior in peer-to-peer energy trading
• Improving market stability using blockchain and AI

Innovation

• Blockchain secures decentralized energy transactions
• AI detects fraud in peer-to-peer energy trading
• Machine learning ensures market reliability and stability