Abstract
Since the Paris Agreement in 2015, there has been growing interest in global warming around the world, and accelerated measures to global warming are required to realize a decarbonized society by declaring the goal of carbon neutrality. As one of the measures, Demand Response (DR) are being actively introduced and provided. DR is a system whereby electric power companies pay incentives through transactions to consumers who cooperate in saving electricity during peak periods of electricity demand, thereby reducing peak electricity demand. Electricity demand for individual buildings is easily affected by seasonal fluctuations, the presence or absence of events, and other factors, and the occurrence of electricity demand peaks tends to be irregular, so highly accurate electricity demand forecasting is needed. In this study, we focus on the educational facilities such as University campus. The objective of this study is to use machine learning to construct a highly accurate forecasting model for not only the steady electricity demand in daily life, but also the characteristic electricity demand during events.
