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This paper addressed some theoretical and practical issues relevant to the problem of power system load modeling and identification. Two identification techniques are developed in the theoretical framework of stochastic system identification. The identification techniques presented in this paper belong to the family of output error models; both techniques are based on well-established equations describing load recovery mechanisms having a commonly recognized physical appeal. Numerical experiments with artificially created data were first performed on the proposed techniques and the estimates obtained proved to be asymptotically unbiased and achieved the corresponding Crame´r-Rao lower bound. The proposed techniques were then tested using actual field measurements taken at a paper mill, and the corresponding results were used to validate a commonly used aggregate load model. The results reported in this paper indicate that the existing load models satisfactorily describe the actual behavior of the physical load and can be reliably estimated using the identification techniques presented herein.