Simulation of Blockchain based Power Trading with Solar Power Prediction in Prosumer Consortium Model

Prosumer consortium energy transactive models can be one of the solutions for energy costs, increasing performance and for providing reliable electricity utilizing distributed power generation, to a local group or community, like a university. This research study demonstrates the simulation of block...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-07
Main Authors: Kaung Si Thu, Ongsakul, Weerakorn
Format: Article
Language:English
Subjects:
Blockchain
Computer simulation
Consortia
Cryptography
Decision making
Economic forecasting
Electric power demand
Electric power distribution
Electricity consumption
Electrification
Energy costs
Neural networks
Solar energy
Training
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Prosumer consortium energy transactive models can be one of the solutions for energy costs, increasing performance and for providing reliable electricity utilizing distributed power generation, to a local group or community, like a university. This research study demonstrates the simulation of blockchain based power trading, supplemented by the solar power prediction using MLFF neural network training in two prosumer nodes. This study can be the initial step in the implementation of a power trading market model based on a decentralized blockchain system, with distributed generations in a university grid system. This system can balance the electricity demand and supply within the institute, enable secure and rapid transactions, and the local market system can be reinforced by forecasting solar generation. The performance of the MLFF training can predict almost 90% accuracy of the model as short term ahead forecasting. Because of it, the prosumer bodies can complete the decision making before trading to their benefit.
ISSN:2331-8422
DOI:10.48550/arxiv.2007.11885