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

块链 计算机科学 透明度(行为) 计算机安全 能源消耗 智能合约 分布式计算 人工智能 工程类 电气工程
作者
Athira Jayavarma,P. Preetha,Manjula G. Nair
出处
期刊:Smart Science [Taylor & Francis]
卷期号:12 (1): 105-120 被引量:11
标识
DOI:10.1080/23080477.2023.2270820
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
waa完成签到 ,获得积分10
刚刚
纯真毛豆完成签到,获得积分20
刚刚
刚刚
慕青应助科研的神龙猫采纳,获得10
2秒前
诸葛藏藏发布了新的文献求助10
2秒前
2秒前
斯图伊发布了新的文献求助10
2秒前
xyy完成签到,获得积分10
3秒前
echo发布了新的文献求助10
3秒前
小蘑菇应助LMN采纳,获得10
4秒前
4秒前
4秒前
Ylang发布了新的文献求助10
5秒前
斯文败类应助bdxw采纳,获得50
5秒前
爆米花应助小白采纳,获得10
5秒前
情怀应助木鱼采纳,获得10
6秒前
6秒前
思源应助美丽的老头采纳,获得10
6秒前
李健的小迷弟应助bling采纳,获得10
6秒前
纯真的风应助寸心台水采纳,获得20
7秒前
7秒前
8秒前
Elvira发布了新的文献求助10
8秒前
ding应助薛谔没有腚采纳,获得10
8秒前
闵SUGA完成签到,获得积分10
8秒前
123发布了新的文献求助10
9秒前
Leyi完成签到,获得积分10
9秒前
Criminology34应助理理理理采纳,获得10
9秒前
上官若男应助Wxj246801采纳,获得10
9秒前
10秒前
10秒前
CT发布了新的文献求助10
10秒前
科研通AI6.1应助hsx采纳,获得10
10秒前
Luoling发布了新的文献求助10
12秒前
哈哈哈大赞完成签到,获得积分10
13秒前
14秒前
15秒前
哒哒哒应助CT采纳,获得10
16秒前
16秒前
钙离子完成签到,获得积分10
17秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Les Mantodea de Guyane Insecta, Polyneoptera 2000
Leading Academic-Practice Partnerships in Nursing and Healthcare: A Paradigm for Change 800
基于非线性光纤环形镜的全保偏锁模激光器研究-上海科技大学 800
Pulse width control of a 3-phase inverter with non sinusoidal phase voltages 777
Signals, Systems, and Signal Processing 610
Research Methods for Business: A Skill Building Approach, 9th Edition 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6409282
求助须知:如何正确求助?哪些是违规求助? 8228474
关于积分的说明 17456898
捐赠科研通 5462267
什么是DOI,文献DOI怎么找? 2886339
邀请新用户注册赠送积分活动 1862722
关于科研通互助平台的介绍 1702227