24-hour Electricity Consumption Forecasting for Day Ahead Market with Long Short Term Memory Deep Learning Model

Nalan OzkurtHacer Sekerci OzturaCuneyt GuzelisGuzelis, CuneytOztura, Hacer SekerciOzkurt, Nalan2025-10-062020978-605-01-1331-0978605011331010.1109/ELECO51834.2020.000312-s2.0-85100531136http://dx.doi.org/10.1109/ELECO51834.2020.00031https://gcris.yasar.edu.tr/handle/123456789/5791https://doi.org/10.1109/ELECO51834.2020.00031In 2015 with the foundation of Energy Market Management Inc. AS (EPIAS) the production and pricing of electrical energy began to be made according to consumption estimates. In this study twenty-four hours energy consumption forecasting was made by using long short-term memory method and data was downloded from EPIAS's official web page for the Day Ahead Market. The data set used covers 1500 days between June 2016 and July 2020. The results obtained have been compared with EPIAS's own estimates and actual consumption data.Turkishinfo:eu-repo/semantics/closedAccess24-hour Electricity Consumption Forecasting for Day Ahead Market with Long Short Term Memory Deep Learning ModelConference Object