A secure energy trading in a smart community by integrating Blockchain and machine learning approach

被引:5
作者
Jayavarma, Athira [1 ]
Preetha, P. K. [1 ]
Nair, Manjula G. [1 ]
机构
[1] Amrita Vishwa Vidyapeetham, Dept Elect & Elect Engn, Amritapuri, Kerala, India
关键词
Machine learning; blockchain; Recalling-Enhanced Recurrent Neural Network; peer-to-peer energy trading; smart community; internet of Things; NETWORK; INTERNET;
D O I
10.1080/23080477.2023.2270820
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In today's smart communities, small-scale energy systems are essential for sustainable development and efficient resource management. However, ensuring the confidentiality, safety, and accurate prediction of energy consumption patterns in energy trading is a major challenge. To address these issues, an innovative solution that synergistically combines two cutting-edge technologies: blockchain and machine learning is proposed. This paper unveils a novel approach that harmoniously merges blockchain with the Recalling-Enhanced Recurrent Neural Network (RERNN) to revolutionize energy trading systems called 'Blockchain-Enhanced Energy Trading with Recalling-Enhanced Recurrent Neural Network (BET-RERNN).' Data from IoT-enabled smart devices is securely stored in blockchain blocks, ensuring data integrity and immutability. Blockchain's decentralized nature creates a trust-less environment for energy trading, protecting the privacy and anonymity of participants while maintaining transparency. At the heart of our system lies the advanced machine-learning capabilities of the RERNN model. By processing the data stored on the blockchain, RERNN accurately predicts optimal power generation for small-scale energy systems, enabling smart communities to make informed decisions and optimize their energy consumption. The BET-RERNN scheme provides a plethora of strengths. First, participants can securely engage in energy trading without compromising sensitive information, fostering a more resilient and efficient market. Second, blockchain technology ensures that all energy-related data is protected from tampering and unauthorized access, ensuring system reliability and trust. An in-depth comparison of RERNN's performance to traditional General Regression Neural Network (GRNN) and Gradient Boost Decision Tree (GBDT) methods is conducted. To verify the strategy's effectiveness, MATLAB simulations are employed, demonstrating its real-world applicability and scalability. By combining blockchain and machine learning, a secure and privacy-preserving smart community is established, promoting sustainable energy practices for a greener future.
引用
收藏
页码:105 / 120
页数:16
相关论文
共 47 条
[1]   A novel image steganography technique based on quantum substitution boxes [J].
Abd EL-Latif, Ahmed A. ;
Abd-El-Atty, Bassem ;
Venegas-Andraca, Salvador E. .
OPTICS AND LASER TECHNOLOGY, 2019, 116 :92-102
[2]   An internet of things-based smart warehouse infrastructure: design and application [J].
Affia, Ifadhila ;
Aamer, Ammar .
JOURNAL OF SCIENCE AND TECHNOLOGY POLICY MANAGEMENT, 2022, 13 (01) :90-109
[3]  
AJ S., 2022 IEEE 19 IND COU
[4]   A Case Study for Blockchain in Manufacturing: "FabRec": A Prototype for Peer-to-Peer Network of Manufacturing Nodes [J].
Angrish, Atin ;
Craver, Benjamin ;
Hasan, Mahmud ;
Starly, Binil .
46TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE, NAMRC 46, 2018, 26 :1180-1192
[5]  
Babu T., 2018, SSRN ELECT J, DOI [10.2139/ssrn.3167771, DOI 10.2139/SSRN.3167771]
[6]  
Bibri S.E., 2019, City Territory Archit., V6, P1, DOI [10.1186/s40410-019-0102-3, DOI 10.1186/S40410-019-0102-3, 10.1186/S40410-019-0102-3/TABLES/3, DOI 10.1186/S40410-019-0102-3/TABLES/3]
[7]   Blockchains and Smart Contracts for the Internet of Things [J].
Christidis, Konstantinos ;
Devetsikiotis, Michael .
IEEE ACCESS, 2016, 4 :2292-2303
[8]   Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Systems [J].
Eseye, Abinet Tesfaye ;
Lehtonen, Matti ;
Tukia, Toni ;
Uimonen, Semen ;
Millar, R. John .
IEEE ACCESS, 2019, 7 :91463-91475
[9]   SF-OEAP: Starvation-Free Optimal Energy Allocation Policy in a Smart Distributed Multimicrogrid System [J].
Funde, Nitesh A. ;
Dhabu, Meera M. ;
Deshpande, Parag S. ;
Patne, Nita R. .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (11) :4873-4883
[10]   A recalling-enhanced recurrent neural network: Conjugate gradient learning algorithm and its convergence analysis [J].
Gao, Tao ;
Gong, Xiaoling ;
Zhang, Kai ;
Lin, Feng ;
Wang, Jian ;
Huang, Tingwen ;
Zurada, Jacek M. .
INFORMATION SCIENCES, 2020, 519 :273-288