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

Ioannis K. Bazionis, Markos A. Kousounadis-Knudsen, Theodoros Konstantinou and Pavlos S. Georgilakis
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Ioannis K. Bazionis: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece
Markos A. Kousounadis-Knudsen: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece
Theodoros Konstantinou: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece
Pavlos S. Georgilakis: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece

Energies, 2021, vol. 14, issue 18, 1-23

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.

Keywords: lower upper bound estimation; particle swarm optimization; prediction intervals; seasonality; wind power probabilistic forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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