BARET: Blockchain and Artificial Intelligence Enabled Renewable Energy Trading Framework

Renewable Energy (RE) plays an important role in minimizing the exploitation of natural resources. Also, it helps to reduce the emission of harmful gases in our surroundings. The Blockchain-based Renewable Energy Trading (RET) platform is a futuristic, sustainable solution that allows us to trade RE with less involvement of intermediaries. However, an increase in transaction volume on these platforms leads to delays in transaction confirmation time. Thus, the interest of producers and consumers loses pace. Therefore, we need an alternative solution that can handle the transaction burden without being affected by the increase in producers and consumers participating in the Blockchain-enabled RET platform. In this context, this paper proposed a Blockchain and Artificial Intelligence Enabled Transactive Renewable Energy Trading Framework called BARET. The BARET framework uses the Blockchain and Artificial Intelligence (AI) engine. The Blockchain uses smart contracts to provide trust and transparency in the BARET framework. Also, integrating the Priority-based Double Action using the K-Means clustering (PDA-KC) mechanism to prioritize the number of transactions that are used to store on-chain appropriately. The results demonstrate the suitability of the proposed BARET framework in real-time through a use case. Additionally, a case study using smart contracts and PDA-KC-based results is presented to show the working of the proposed framework. Finally, a tablebased comparison indicates the advantages of the BARET framework over state-of-the-art works.

Authors

Institutions

Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/ants63515.2024.10898469
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

BARET: Blockchain and Artificial Intelligence Enabled Renewable Energy Trading Framework

Bhawana, Richa Golash
Blockchain Technology Applications and Security
article

BARET: Blockchain and Artificial Intelligence Enabled Renewable Energy Trading Framework

Bhawana, Richa Golash
article en

Abstract

Renewable Energy (RE) plays an important role in minimizing the exploitation of natural resources. Also, it helps to reduce the emission of harmful gases in our surroundings. The Blockchain-based Renewable Energy Trading (RET) platform is a futuristic, sustainable solution that allows us to trade RE with less involvement of intermediaries. However, an increase in transaction volume on these platforms leads to delays in transaction confirmation time. Thus, the interest of producers and consumers loses pace. Therefore, we need an alternative solution that can handle the transaction burden without being affected by the increase in producers and consumers participating in the Blockchain-enabled RET platform. In this context, this paper proposed a Blockchain and Artificial Intelligence Enabled Transactive Renewable Energy Trading Framework called BARET. The BARET framework uses the Blockchain and Artificial Intelligence (AI) engine. The Blockchain uses smart contracts to provide trust and transparency in the BARET framework. Also, integrating the Priority-based Double Action using the K-Means clustering (PDA-KC) mechanism to prioritize the number of transactions that are used to store on-chain appropriately. The results demonstrate the suitability of the proposed BARET framework in real-time through a use case. Additionally, a case study using smart contracts and PDA-KC-based results is presented to show the working of the proposed framework. Finally, a tablebased comparison indicates the advantages of the BARET framework over state-of-the-art works.

Bennett University (IN)
Affordable and clean energy
Openalex Percentile: Top 56%
Blockchain Technology Applications and Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.