Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression

Abstract

This paper presents an efficient non-iterative hybrid empirical mode decomposition (EMD) and kernel ridge regression (KRR) for significantly accurate short-term wind speed and wind power prediction. The original non-linear and non-stationary wind speed and wind power time series data are decomposed using EMD to isolate the mutual effects between different components. The proposed EMD-KRR model is tested for predicting wind speed and wind power time series data over a time horizon spanning intervals of 10 min, 30 min, 1 h and 3 h ahead, respectively. Further the performance of EMD-KRR prediction model is compared with two other widely used non-iterative prediction models like EMD based Random vector functional link network (RVFL), and EMD based Extreme Learning Machine (ELM). Also an iterative Mutated Firefly Algorithm with Global Optima concept (MFAGO) optimized RVFL network is used for comparison to prove the advantages of non-iterative models over the iterative ones. The performance metrics of the proposed EMD-KRR and EMD-RVFL confirm the effectiveness and precision in producing an accurate forecast of both wind speed and wind power in comparison to all other prediction models using the wind power data from three real world wind farms. Further a fast reduced version of the EMD-KRR is presented in the paper to reduce the computational overhead substantially using randomly selected support vectors from the data set while resulting in a reasonably accurate forecast.