  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.
