Power 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.