Pricing in accordance with the IOT supported demand predictions in the spot electricity market

In the energy markets, the formation of the market exchange price is realized according to the supply and demand forecasts; demand forecasts that cannot be realized with high accuracy trigger irregular formation of prices. In this study, electricity prices in spot energy markets, Internet of Things (IoT) and new technological approaches are examined. It is aimed to contribute to the reduction of supply and demand estimation error rates which are the source of forecasting errors in day ahead and in-day pricing in spot energy markets. For this purpose, the energy market is examined in the perspective of the Internet of Things (IoT) and the smart grid systems approach. Moreover, in the application section of the study, an application is made to determine whether it is possible to make the estimation studies more effectively with these approaches. In this study, by performing artificial neural networks, the performance of low-frequency data sets which contain daily observations with less observations and high-frequency data sets which contain hourly values with more observations for 2016 and 2017, are compared. The results indicate that more accurate estimation can be performed and estimation errors can be minimized by higher frequency data. Keywords: Spot energy markets, internet of things (IOT), pricing, price forecast