The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

Abstract

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction

“…Quantum-Behaved PSO and the Firefly Algorithms could also be used to identify training stations that have been tested to hybridize with the OSELM (e.g., 101 , 102 ). Future works could apply, empirical wavelet transform-EWT 103 , empirical mode decomposition-EMD 104 , and singular value decomposition-SVD 105 , as data pre-processing tools in modeling and predicting crop yields.…”

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

“…Aggregating all the individual predictions results in the final forecast [1]. Commonly used decomposition approaches are Wavelet Transformation [37], Empirical Mode Decomposition [38], Variational Mode Decomposition [17], and their variants [39]. The individual prediction models can either be statistical, artificial intelligence, or bothfor instance, WT-ARIMA [7], EMD-PE-ANN [38], VMD-PRBF-ARMA-E [40], EEMD-PSF-ARIMA [39], WT-VMD-DLSTM-AT [41], EWT-BiDLSTM [42].…”

Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

“…The combination prediction model is a combination of more than two kinds of models, combined with the advantages of various models, and has achieved good results in wind power forecasting (Lin et al, 2019; Wang et al, 2020; Zhang et al, 2019). On the other hand, some technologies include wavelet transform (Liu et al, 2018), empirical model decomposition (Ding and Meng, 2020; Tian et al, 2018 ), and ensemble empirical mode decomposition (Bokde et al, 2020) are utilized to decompose the wind power time series. For different components, appropriate prediction models are selected according to their characteristics.…”