Ertugrul, Omer FarukTagluk, Mehmet Emin2024-08-042024-08-042017978-1-5090-5938-6https://hdl.handle.net/11616/978865th International Istanbul Smart Grid and Cities Congress and Fair (ICSG) -- APR 19-21, 2017 -- Istanbul, TURKEYPower load estimation, especially short-term power load estimation, plays an important role in the management of a power system in terms of system security and electricity costs. Therefore, estimation of short-term power load accurately is a popular research issue. In this paper, the generalized behavioral learning method (GBLM), a method developed based on human's behavioral learning theories, was employed to estimate short-term power load. The datasets that belong to houses B and C were employed in the estimation process. Achieved results by GBLM and extreme learning machine (ELM) ELM were compared. It is showed that GBLM estimates short-term power load with a higher success rate than ELM.eninfo:eu-repo/semantics/closedAccessGeneralized Behavioral LearningExtreme Learning MachinePower Load EstimationSmall HouseShort-Term Power LoadConference Object85892-s2.0-85023162291N/AWOS:000411752300016N/A