Short-Term Wind Speed Interval Prediction using LUBE based Quasi-Recurrent Neural Network

Abstract

Accurate wind speed forecasting is an extremely difficult task and therefore less accurate forecasts may sometimes make the power systems vulnerable. Interval predictions on the other hand may help in maximizing the usage of integrated wind energy, as well as to reduce the adverse effects of the uncertainties introduced by the random fluctuations of wind, to the power systems. In this paper, we propose a novel wind speed interval prediction model by integrating lower upper bound estimation method (LUBE) into a quasi-recurrent neural network (QRNN). LUBE is a non-differentiable loss function requiring usage of meta-heuristic optimization techniques for training the model. By incorporating two objective functions, we proposed a new form of LUBE, using which the network can be trained with conventional stochastic gradient descent. The proposed model is evaluated for eight different datasets distributed among two wind fields. Results obtained from the computational experiments reveals that the proposed method generated narrow intervals with high coverage which helps it in achieving an improvement of 33% in coverage width criterion over the traditional models.