Non-Fungible Token Infrastructure based Load Forecasting Learning Model

Abstract

The NFT (Non-Fungible Token) market has soared in recent years. The concept of NFT originally originated from Ethereum’s token standard and aims to distinguish each token with a distinguishable symbol. Tokens of this type can bind virtual/digital attributes to unique identification items. These features can be used to bind AI learning outcomes to unique identification items. A simple load forecast learning model can be produced with simple PC performance. However, if the structure is complicated or the number of learning epoch increases, the value and importance of the model are made difficult. This study was conducted to promote developer participation in complex model development and to protect works.