A novel hybrid approach based on cuckoo search optimization algorithm for short‐term wind speed forecasting

Zhang, Kequan; Zhi, Qu; Wang, Jianzhou; Zhang, Wenyu; Yang, Feiyue

doi:10.1002/ep.12533

Cited by 23 publications

(13 citation statements)

References 41 publications

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“…In Refs. [10,19,27,46] and [60], the IMFs were categorized into two bands with high and low frequency components. These articles stated the IMFs with lower frequency bands represent the central tendency of the data and highly regular pattern which shows the accurate characteristics of the original data, whereas the IMFs with higher frequencies contain large quantity of noisy signals, which mainly reflects the random information that led to a great disturbance for prediction precision of wind data.…”

Section: Intrinsic Mode Functionsmentioning

confidence: 99%

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

et al. 2019

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Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

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Section: Intrinsic Mode Functionsmentioning

confidence: 99%

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

et al. 2019

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“…Investigators have implemented many different methods to ameliorate the forecasted energy of wind speed . The short‐term prediction showed a high accuracy rate compared with other steady predictions . The price management in markets by improving daily‐ahead market (DM) and fulfilling markets bidding in adjusted market (AM) is one of the methods suggested for increasing market‐rate profits.…”

Section: Introductionmentioning

confidence: 99%

“…2 The short-term prediction showed a high accuracy rate compared with other steady predictions. 3 The price management in markets by improving daily-ahead market (DM) and fulfilling markets bidding in adjusted market (AM) is one of the methods suggested for increasing market-rate profits. Afshar et al4 presents a compound energy to the markets, aiming to encourage the generators of a natural resource to adapt their generated power to market prices.…”

Section: Introductionmentioning

confidence: 99%

Integrated bidding strategy of distributed energy resources based on novel prediction and market model

Lin

Gauzily

2020

Int J Energy Res

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Summary This paper presents an integrated operation model of renewable energy, distributed generators (DGs), energy storage, and demand response to overcome the challenges of renewable energy market participation. Additionally, an accurate prediction model for wind, photovoltaic (PV) power, and market price is introduced based on ensemble empirical model decomposition. In this model, radial basis function neural network (RBFNN) is used for the forecasting of subsignals from the analysis model. The training model of RBFNN is shaped based on the B‐water cycle algorithm (WCA) optimization method. Additionally, by considering the adjusted market, demand response, and uncertainties analysis, this method reduced the economic damages of generators and loads. A case study test has consisted of a wind turbine, photovoltaic energy, fuel cell energy, demand response, and energy storage.

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“…Yang proposed a BP neural network image restoration algorithm optimized by Gray Wolf algorithm, whose results showed that the algorithm had faster convergence speed and higher restoration accuracy than the Genetic Algorithm-Back Propagation (GA-BP) algorithm [15].Considering the high complexity and volatility of the icing thickness, most historical literature directly model and predict the icing thickness, so the effect is not ideal. Therefore, this paper uses Ensemble Empirical Mode Decomposition (EEMD) method for the first time to decompose the icing thickness, which effectively solves the problem of mode aliasing, retains the real signal to the maximum extent, builds the prediction model based on the decomposition of the original signal, and can effectively improve the accuracy [16][17][18][19][20][21][22].From another perspective, as transmission line icing thickness is a time series change, two prediction methods were proposed to predict accuracy icing thickness data. One is based on historical data of icing thickness, but as the phenomenon of ice coating on the transmission line is a physical phenomenon determined by various factors, the prediction of this method is not accurate.…”

mentioning

confidence: 99%

“…Considering the high complexity and volatility of the icing thickness, most historical literature directly model and predict the icing thickness, so the effect is not ideal. Therefore, this paper uses Ensemble Empirical Mode Decomposition (EEMD) method for the first time to decompose the icing thickness, which effectively solves the problem of mode aliasing, retains the real signal to the maximum extent, builds the prediction model based on the decomposition of the original signal, and can effectively improve the accuracy [16][17][18][19][20][21][22].…”

mentioning

confidence: 99%

Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine

2019

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The ice coating on the transmission line is extremely destructive to the safe operation of the power grid. Under natural conditions, the thickness of ice coating on the transmission line shows a nonlinear growth trend and many influencing factors increase the difficulty of forecasting. Therefore, a hybrid model was proposed in this paper, which mixed Ensemble Empirical Mode Decomposition (EEMD), Random Forest (RF) and Chaotic Grey Wolf Optimization-Extreme Learning Machine (CGWO-ELM) algorithms to predict short-term ice thickness. Firstly, the Ensemble Profit Mode Decomposition model was introduced to decompose the original ice thickness data into components representing different wave characteristics and to eliminate irregular components. In order to verify the accuracy of the model, two transmission lines in 'hunan' province were selected for case study. Then the reserved components were modeled one by one, building the random forest feature selection algorithm and Partial Autocorrelation Function (PACF) to extract the feature input of the model. At last, a component prediction model of ice thickness based on feature selection and CGWO-ELM was established for prediction. Simulation results show that the model proposed in this paper not only has good prediction performance, but also can greatly improve the accuracy of ice thickness prediction by selecting input terminal according to RF characteristics.Energies 2019, 12, 2163 2 of 21 model, Imai model, Goodwin model, etc. [3,4]. However, as important as parameters in the model, which are difficult to collect, the prediction effect of the physical model is poor. The other is based on factors affecting the ice sheet and its development, and intelligent algorithms are used to predict the ice sheet thickness data fit. Lan, D.L and Zheng, Z.H used Generalized Regression Neural Network (GRNN) method to forecast ice thickness, with the results suggesting that the GRNN method shows good performance in ice coating thickness [5]. Xiao-min ma proposed a forecasting model based on grey Support Vector Machine (SVM) short-term ice thickness of transmission line [6], and the results showed that the SVM method can accurately predict the short-term ice thickness. However, GRNN and SVM methods all have systematic errors in simulation. The GRNN model relies too much on the sample data. When the sample data set is not sufficient, the GRNN neural network may lead to poor adaptability; for the SVM method, the kernel function and kernel parameters affect the fitting accuracy and generalization ability to varying degrees.Due to the faster convergence speed and less human interference compared with the traditional neural network, the Extreme Learning Machine (ELM) proposed by huang in 2004 has been widely used in many prediction fields. Zhang ning, huang yuanyu, li wanhua et al. [7][8][9] used the ELM model to predict short-term load based on various influencing factors, whose results showed that compared with the traditional Back Propagation (BP) neural network model, the ELM model coul...

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A novel hybrid approach based on cuckoo search optimization algorithm for short‐term wind speed forecasting

Cited by 23 publications

References 41 publications

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

Integrated bidding strategy of distributed energy resources based on novel prediction and market model

Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine

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