Short-term Wind Speed Forecasting based on LSSVM Optimized by Elitist QPSO

TL;DR

This paper introduces a novel hybrid model combining LSSVM with an improved QPSO algorithm, optimized for better wind speed forecasting accuracy amidst non-stationary and nonlinear data characteristics.

Contribution

It proposes a modified QPSO algorithm utilizing transposon operators to enhance hyperparameter tuning of LSSVM for wind speed prediction.

Findings

The proposed model outperforms PSO and QPSO optimized LSSVM in accuracy.

Enhanced global search capability improves forecasting performance.

Empirical results demonstrate superior results over existing methods.

Abstract

Nowadays, wind power is considered as one of the most widely used renewable energy applications due to its efficient energy use and low pollution. In order to maintain high integration of wind power into the electricity market, efficient models for wind speed forecasting are in high demand. The non-stationary and nonlinear characteristics of wind speed, however, makes the task of wind speed forecasting challenging. LSSVM has proven to be a good forecasting algorithm mainly for time-series applications such as wind data. To boost the learning performance and generalization capability of the algorithm, LSSVM has two hyperparameters, known as the regularization and kernel parameters, that require careful tuning. In this paper, a modified QPSO algorithm is proposed that uses the principle of transposon operators to breed the personal best and global best particles of QPSO and improve global searching capabilities. The optimization algorithm is then used to generate optimum values for the LSSVM hyperparameters. Finally, the performance of the proposed model is compared with previously known PSO and QPSO optimized LSSVM models. Empirical results show that the proposed model displayed improved performance compared to the competitive methods.