This paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model provides wind speed nowcasting using a combination of perturbed observation ensemble networks and artificial neural networks. The model was validated and evaluated via simulation using data that were measured from wind masts. The simulation results show that the proposed model improved the normalized root mean square error by 20.9% compared to other contending approaches. In terms of prediction interval coverage probability, our proposed model shows a 17.8% improvement, all while using a smaller number of neural networks. Furthermore, the proposed model has an execution time that is one order of magnitude faster than other contenders.
To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and interpolation. To check the efficacy of the proposed model, the power output from a real wind farm is predicted and compared with the actual recorded measurements. The results of the comprehensive analysis show that the proposed model outperforms contending models in terms of accuracy and execution time. Therefore, this model can be used by operators to reliably generate a dispatch plan.
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