Abstract
Japan Electric Power Exchange (JEPX) is the only electricity market in Japan that allows transactions for electric power established in 2005. Several commodities are traded there, such as the spot market and the forward market. In particular, the spot market is a major trading market. The price of the spot market changes depending on the relationship between supply and demand. Therefore, it is important to forecast spot market prices to make a supply and demand plan for the next day. This research focused on the factors that determine supply and demand are related to geospatial information—first, deriving the explanatory variables for JEPX spot prices using Geographic Information System (GIS). Then, constructing the spot price forecasting system by machine learning using the derived explanatory variables. By using this system, it is possible to forecast electricity prices with higher accuracy than existing methods.
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