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

Ioannis K Bazionis,Theodoros Konstantinou,Pavlos S Georgilakis,Markos A Kousounadis-Knudsen

doi:10.3390/en14185942

Ioannis K Bazionis, Theodoros Konstantinou + Show 2 more

Open Access

https://doi.org/10.3390/en14185942

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Journal: Energies	Publication Date: Sep 18, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: National Technical University of Athens

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.

Highlights

In regard to dealing with global climate change as well as the increasing global energy needs, turning to renewable energy alternatives has been the focus of researchers in recent years
A particle swarm optimization (PSO) algorithm along with a mutation operator was implemented in order to further optimize the wavelet transform (WT)-lower upper bound estimation (LUBE) methodology
Multi-Objective Methodology The work presented in this paper focuses on minimizing the coverage width criterion (CWC) cost function and on optimizing a single objective problem

Summary

Introduction

In regard to dealing with global climate change as well as the increasing global energy needs, turning to renewable energy alternatives has been the focus of researchers in recent years. Neural networks (NNs) have been introduced and have been excessively used in order to develop accurate wind power forecasting models able to estimate and control wind power generation. Throughout the years, NNs have been used as deterministic forecasting models in order to generate point forecasts and provide the user with an estimated wind power output series, which is as accurate as possible. Such models fail to provide information on the uncertainty of a prediction. Due to the increasing penetration of wind power into power systems, deterministic models cannot always be efficiently used for real-life problems as well as decision-making tasks

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