Peer to Peer Energy Trading Method and System by using Machine learning Algorithm built-in Energy Agent

摘要

A power trading system using an energy agent based on a machine learning algorithm according to an embodiment of the present invention includes the power generation amount of the energy storage system and/or the renewable energy system and the demand for the power generation amount. At least one smart meter measuring an actual value of the load power amount; An information collecting unit that collects relational data related to the amount of power generation and the amount of load on demand; And applying the measured values and the relational data measured by the at least one smart meter to a gradient descent machine learning algorithm to learn and analyze predicted values of the power generation amount and the demand load power amount based on learning data. Based on the analysis result, the charge/discharge schedule of the energy storage system and/or the power generation schedule of the renewable energy system according to the demand load power estimate are calculated, and the time is determined according to the calculated charge/discharge schedule and/or power generation schedule. It includes an artificial intelligence power transaction agent unit that analyzes power surpluses and shortfalls and performs power transactions for power shortfalls.