A WT-LUBE-PSO-CWC Wind Power Probabilistic Forecasting Model for Prediction Interval Construction and Seasonality Analysis

Abstract

Deterministic forecasting models have been used through the years to provide accurate predictive outputs in order to efficiently integrate wind power into power systems. However, such models do not provide information on the uncertainty of the prediction. Probabilistic models have been developed in order to present a wider image of a predictive outcome. This paper proposes the lower upper bound estimation (LUBE) method to directly construct the lower and upper bound of prediction intervals (PIs) via training an artificial neural network (ANN) with two outputs. To evaluate the PIs, the minimization of a coverage width criterion (CWC) cost function is proposed. A particle swarm optimization (PSO) algorithm along with a mutation operator is further implemented, in order to optimize the weights and biases of the neurons of the ANN. Furthermore, wavelet transform (WT) is adopted to decompose the input wind power data, in order to simplify the pre-processing of the data and improve the accuracy of the predictive results. The accuracy of the proposed model is researched from a seasonal perspective of the data. The application of the model on the publicly available data of the 2014 Global Energy Forecasting Competition shows that the proposed WT-LUBE-PSO-CWC forecasting technique outperforms the state-of-the-art methodology in important evaluation metrics.

A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

“…[17] LUBE with optimization algorithm Multi-objective optimization for flood interval prediction using KELM and OCNSGA-II. [16,21,22] Prediction method with data preprocessing…”

“…Indicators serve as a measure of the width of the prediction interval to verify the clarity of the prediction interval. An ideal prediction interval is characterized by a low prediction interval normalized average width (PINAW) or prediction interval normalized range width (PINRW) [16]. The smaller the indicator value, the narrower the prediction interval and the higher the clarity.…”

“…Ref. [16] proposed a wavelet transform and LUBE and artificial neural network (WT-LUBE-ANN) model for interval prediction, combining the PSO and mutation operator with coverage width criterion (CWC) cost function minimization. The model proves effective in accurately anticipating wind power intervals.…”