Very Short-term Wind Energy Forecasting Based on Stacking Ensemble

Title: Very Short-term Wind Energy Forecasting Based on Stacking Ensemble

Authors: Sinvaldo Rodrigues Moreno, Ramon Gomes da Silva, Matheus Henrique dal Molin Ribeiro, Naylene Fraccanabbia, Viviana Cocco Mariani, Leandro dos Santos Coelho

Abstract: Wind power generation is one of the technologies of electric production which still in development in Brazil, however, it already has a great penetration in the national energy matrix, representing 13.98% of the national energy consumption in Brazil. Due to the high level of uncertainty and the chaotic fluctuations in wind speed, predictions of wind energy with high accuracy is a challenge. In this context a stacking ensemble (STACK) model is proposed to forecast the wind power generation of a turbine in a wind farm at Parazinho, RN – Brazil. The proposed model combines four different algorithms as base-learners, such as, eXtreme Gradient Boosting (xgBoost), Support Vector Machine for regression with Linear Kernel (SVR-Linear), Multi-Layer Perceptron with multiple layers (MLP) and K-Nearest Neighbors (K-NN), and one algorithm as meta-learner – Support Vector Machine for regression with Radial Basis Function Kernel (SVR-RBF). To access the performance of adopted methodology, the results of STACK are compared with the results of the base-learners. Four performance measure criteria, as well as statistical tests are adopted. As results, STACK reached better results in all performance measures. Indeed, STACK and SVR-Linear are statistically equals. According to these results, applying the STACK proposed model indeed improved the forecasting when comparing with the other algorithms tested individually.

Key-words: Wind energy, forecasting, time series, machine learning, stacking ensemble.

Pages: 7

DOI code: 10.21528/CBIC2019-22

PDF file: CBIC2019-22.pdf

BibTeX file: CBIC2019-22.bib