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

TL;DR

This paper demonstrates a blockchain-based power trading simulation for prosumer communities, integrating solar power prediction with neural networks to enable secure, efficient, and predictive local energy markets.

Contribution

It introduces a novel simulation model combining blockchain energy trading with MLFF neural network solar prediction for prosumer communities.

Findings

Achieved nearly 90% accuracy in short-term solar power forecasting.

Enabled secure, rapid transactions within a decentralized energy market.

Provided a foundation for implementing blockchain-based local energy trading systems.

Abstract

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.