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Precise wind resource assessment is one of the more imminent challenges. In the present work, we develop an adaptive approach to wind speed forecasting. The approach is based on a combination of the efficient apparatus of non-stationary time series of wind speed retrospective data analysis based on the Hilbert-Huang transform and machine learning models. Models that are examined include neural networks,...
A problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The input signal is decomposed into orthogonal basis functions using the Hilbert-Huang transform. The hybrid-genetic algorithm is applied to optimal training...
The problem of forecasting of electicity prices is addressed in terms of joint approach employing the general regression artificial neural network and empirical mode decomposition approaches (EMD) which is part of Hilbert-Huang transform. The application of developed approach to day-ahead hourly time series has demonstrated the whole accuracy increase as well as peaks prediction.
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