Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks

Liu, Hui; Tian, Hong; Pan, Di; Li, Yan Fei

doi:10.1016/j.apenergy.2013.02.002

Cited by 305 publications

(120 citation statements)

References 37 publications

Supporting

Mentioning

116

Contrasting

Unclassified

Order By: Relevance

“…Liu et al [38] applied wavelets and wavelet packets to preprocess the original wind speed data and concluded that the wavelet packet-ANN had the best performance compared with other traditional models. Ghasemi et al [39] proposed a novel hybrid algorithm for electricity price and load forecasting, including the flexible wavelet packet transform (FWPT), conditional mutual information (CMI), artificial bee colony (ABC), support vector machine (SVM) and ARIMA.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

View full text Add to dashboard Cite

Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

show abstract

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

“…The IMF or trend term at each time maintains the natural dyadic filter window; therefore, the final average also maintains this type of quality, and the mixing mode problem is solved. Its pseudo-code is described below [38]. The pseudo-code of EEMD is described in Appendix A.…”

Section: Remarkmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

View full text Add to dashboard Cite

show abstract

“…Assuming that the fluctuating wind is a stationary stochastic Gaussian process, the stochastic characteristics can be described by the power spectral density (PSD) function [31]. Then, the energy distributions of the PSD function of the fluctuating wind are explored by the wavelet method [32] adopting the Hanning windows [33]. Therefore, the main thoughts of the MS method are listed as follows: the parts of the energy concentration are simulated by the WAWS method [15] and the parts of the energy dispersion are generated by the AR method [16].…”

Section: The Methods For Simulating Fluctuating Windmentioning

confidence: 99%

Numerical Simulation of Fluctuating Wind Effects on an Offshore Deck Structure

Zhou

Han

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

The offshore structures that play a vital role in oil and gas extraction are always under complicated environmental conditions such as the random wind loads. The structural dynamic response under the harsh wind is still an important issue for the safety and reliable design of offshore structures. This study conducts an investigation to analyze the wind-induced structural response of a typical offshore deck structure. An accurate and efficient mixture simulation method is developed to simulate the fluctuating wind speed, which is then introduced as the boundary condition into numerical wind tunnel tests. Large eddy simulation (LES) is utilized to obtain the time series of wind pressures on the structural surfaces and to determine the worst working condition. Finally, the wind-induced structural responses are calculated by ANSYS Parametric Design Language (APDL). The numerically predicted wind pressures are found to be consistent with the existing experimental data, demonstrating the feasibility of the proposed methods. The wind-induced displacements have the certain periodicity and change steadily. The stresses at the top of the derrick and connections between deck and derrick are relatively larger. These methods as well as the numerical examples are expected to provide references for the wind-resistant design of the offshore structures.

show abstract

“…Different hybrid wind speed prediction models have been proposed in the literature in order to benefit from the unique capability of single models [27][28][29][30][31][32][33][34][35][36][37]. Salcedo-Sanz et al [27] proposes the hybridization of the fifth-generation mesoscale physical forecasting model (MM5) with neural networks for shortterm wind speed prediction of a wind park located at Albacete in Spain.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

View full text Add to dashboard Cite

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

show abstract

Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks

Cited by 305 publications

References 37 publications

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Numerical Simulation of Fluctuating Wind Effects on an Offshore Deck Structure

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Contact Info

Product

Resources

About