A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. (1st February 2019)

• Record Type: Journal Article
• Authors: Qian, Zheng; Pei, Yan; Zareipour, Hamidreza; Chen, Niya
• Is Part Of: Applied energy. Volume 235(2019)
• Page Start: 939
• Page End: 953
• Publication Date: 2019-02-01
• Subjects: Wind forecasting -- Hybrid models -- Decomposition-based models; ABC Artificial Bee Colony -- ACF Auto Correlation Function -- AIC Akaike's Information Criterion -- ANFIS Adaptive Neuro Fuzzy Inference System -- ANN Artificial Neural Network -- AR Auto Regression -- ARIMA Auto-Regressive Integrated Moving Average -- ARMA Auto Regression Moving Average -- ASD Atomic Sparse Decomposition -- BGA Bernaola Galvan Algorithm -- BP Back Propagation -- CEEMD Complementary Ensemble Empirical Mode Decomposition -- CEEMDAN Complete Ensemble Empirical Mode Decomposition with Adaptive Noise -- CFD Computational Fluid Dynamic -- CNN Convolutional Neural Network -- CSO Crisscross Optimization -- DBN Deep Belief Network -- EEMD Ensemble Empirical Mode Decomposition -- EMD Empirical Mode Decomposition -- ENN Elman Neural Network -- ESM Exponential Smoothing Method -- EWT Empirical Wavelet Transform -- FA Fuzzy ARTMAP -- FAC First-order Adaptive Coefficient -- GA Genetic Algorithm -- GM Grey Model -- GP Gaussian Processes -- GRNN Generalized Regression Neural Network -- HNN Hybrid Neural Networks -- IMF Intrinsic Mode Function -- l ITD Intrinsic Time-scale Decomposition -- KL Kullback-Leibler -- KNN k Nearest Neighbor -- MEA Mind Evolutionary Algorithm -- MIV Mean Impact Value -- MLP Multi-Layer Perceptron -- NWP Numerical Weather Prediction -- PACF Partial Auto Correlation Function -- PRC Proper Rotation Component -- PSO Particle Swarm Optimization -- RBF Radial Basis Function -- RMSE Root Mean Square Error -- RVM Relevance Vector Machine -- SAC Second-order Adaptive Coefficient -- SAM Seasonal Adjust Method -- SSA Singular Spectrum Analysis -- SVM Support Vector Machine -- WD Wavelet Decomposition -- WNN Wavelet Neural Network -- WPD Wavelet Packet Decomposition -- VMD Variational Mode Decomposition; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.10.080
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Decomposition-based hybrid wind energy forecasting models are reviewed. Techniques improving the performance of decomposition-based models are discussed. Challenges in the application of decomposition-based models are discussed. Potential directions improving the accuracy of these models are discussed. Abstract: With the continuous growth of wind power integration into the electrical grid, accurate wind power forecasting is an important component in management and operation of power systems. Given the challenging nature of wind power forecasting, various methods are presented in the literature to improve wind power forecasting accuracy. Among them, combining different techniques to construct hybrid models has been frequently reported in the literature. Decomposition-based models are a family of hybrid models that firstly decompose the wind speed/power time series into relatively more stationary subseries, and then build forecasting models for each subseries. In this paper, we present a comprehensive review of decomposition-based wind forecasting methods in order to explore their effectiveness. Decomposition-based hybrid forecasting models are classified into different groups based on the decomposition methods, such as, wavelet, empirical mode decomposition, seasonal adjust methods, variational mode decomposition, intrinsic time-scale decomposition, and bernaola galvan algorithm. We discuss decomposition methods in the context of alternative forecasting algorithms, and explore the challenges of each method. Comparative analysis of various decomposition-based models is also provided. We also explore current research activities and challenges, and identify potential directions for future research on this subject.