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The paper presents the two-stages adaptive approach for short-term forecast of parameters of expected operating conditions. The first stage involves decomposition of the time series into intrinsic modal functions and subsequent application of the Hilbert transform. During the second stage the computed modal functions and amplitudes are employed as input functions for artificial neural networks. Their optimal combinations is constructed using methods of simulated annealing and neural-genetic input selection approach. The efficiency of developed approach is demonstrated on real time the problem of forecasting power flow and voltage level.