Bodur, İdrisÇelik, EmreÖztürk, Nihat2023-07-262023-07-262021978-1-6654-4524-52377-6897https://doi.org/10.1109/ICRERA52334.2021.9598773https://hdl.handle.net/20.500.12684/1319710th IEEE International Conference on Renewable Energy Research and Applications (ICRERA) -- SEP 26-29, 2021 -- Istanbul, TURKEYElectricity energy is produced from another energy source like fossil source such as oil, coil, natural gas renewable energy sources such as hydraulic, wind, solar. Their storage in high amount is a problematic issue. Therefore, the balance between the power generation and demanded power must be satisfied at all times. This is an obligation especially for companies that generate, transmit and distribute electrical energy. In this paper, a short term load demand forecasting based on a long short term memory (LSTM) is addressed, which may help planning operators for Turkish electricity market. The results of advocated approach were compared by the ones based on recurrent neural network As a result, it is found that the proposed LSTM approach can predict especially daily and weekly demands with an accuracy more than 90%.en10.1109/ICRERA52334.2021.9598773info:eu-repo/semantics/closedAccessShort Term Load Forecasting; Long-Short-Term Memory; Recurrent Neural NetworkConference Object1711742-s2.0-85123168844WOS:000761616700027N/AN/A