We compare structural different methods of the artificial intelligence for wind power prediction modeling and build additionally ensembles of the models. As input variables for these prediction methods weather data of a numerical weather prediction model are used. The performance of the presented methods is compared to the predictions of the neural network based model.
This paper describes different methods to estimate the uncertainty of wind power forecasts in terms of prediction intervals. The single methods and an ensemble average model have been applied to shortest-term wind power forecasts (forecast horizon = 1, 2, 4 & 8 h) of 62 spatially
The large-scale integration of wind generation imposes several difficulties in the management of power systems. Wind power forecasting up to a few days ahead contributes to a secure and economic power system operation. Prediction models of today are mainly focused on spot or prob
For a sustainable integration of wind power into the electricity grid, precise and robust predictions are required. With increasing installed capacity and changing energy markets, there is a growing demand for short-term predictions. Machine learning methods can be used as a pure
In this work, we propose the use of support vector regression ensembles for wind power prediction. Ensemble methods often yield better classification and regression accuracy than classical machine learning algorithms and reduce the computational cost. In the field of wind power g
The time series of wind power is influenced by many external factors, showing strong volatility and randomness. Aiming at the problem low prediction accuracy series, this paper proposes a short-term framework based on two-layer decomposition combination ensemble model deep networ
To ensure the safe and stable operation of power systems, accurate prediction wind generation is particularly important. However, due to randomness, fluctuation, intermittency energy, as well challenges in determining hyperparameters gated recurrent unit (GRU) network, this paper