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Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed

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Abstract

Short-term wind speed forecasting has become an important technology to utilize sustainable energy, reduce the impact of wind power grid and improve the control of wind turbines. Current forecasting models based on individual algorithm and hybrid optimization algorithm could be applied to a variety of wind speed forecast. However, these algorithms ignored the limitation of optimizing parameter and the use of recent data, which may result in forecasting poor accuracy. In this paper, integrated approaches, combining singular spectrum analysis technique, cuckoo search algorithm (CSA) and harmony search algorithm (HSA), and back propagation neural network (BPNN), were introduced for conducting short-term wind speed forecasting. Firstly, the SSA technique is used for identifying and extracting instable components from raw wind speed signals. Then, the parameters of BPNN are employed to be optimized by the CSA/HSA, which improve precision of forecast results. Finally, the BPNN is utilized to deal with the wind speed series. The proposed hybrid model is conducted by the wind speed at three stations located in Penglai, China. Our experiment reveals that the proposed hybrid model can generate a more accurate, reliable and robust result and SSA technique is found to be superior technique to preprocess wind speed series. Furthermore, we compare to the forecast results with SSA and non SSA denoising procedure and found that hybrid model with SSA technique outperforms other prediction model.

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Abbreviations

SSA:

Singular spectrum analysis

HSA:

Harmony search algorithm

CSA:

Cuckoo search algorithm

BPNN:

Back propagation neural network

NWP:

Numerical weather predication

FART:

Fuzzy adaptive resonance theory

ANN:

Artificial neural networks

EMD:

Empirical mode decomposition

KF:

Kalman filter

SVM:

Support vector machine

ARMA:

Auto-regressive moving average

NARX:

The nonlinear autoregressive model

ARIMA:

Autoregressive integrated moving average

ABC:

The artificial bee colony algorithm

FNN:

Fuzzy neural network

CSA-BPNN:

BPNN is optimized by cuckoo search algorithm

HSA-BPNN:

BPNN is optimized by harmony search algorithm

SSA-BPNN:

Forecasting results of BPNN denoised by SSA

SSA-CSA-BPNN:

Cuckoo search to optimize BPNN denoised by SSA

SSA-HSA-BPNN:

Harmony search to optimize BPNN denoised by SSA

PM:

Persistent model

MLR:

Multiple linear regression

WT:

Wavelet transform

GA:

Genetic algorithm

MLP:

Multilayer perception

PSO:

Particle swarm optimization

SA:

Simulated annealing

RVM:

Relevance vector machine

DM test:

Diebold–Mariano test

MA:

Moving-average

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 71573034). All the authors do not have any possible conflicts of interest.

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Authors and Affiliations

  1. School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China

    Ping Jiang & Ranran Li

  2. Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China

    Kequan Zhang

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  1. Ping Jiang
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Correspondence to Ranran Li.

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Jiang, P., Li, R. & Zhang, K. Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed. Neural Comput & Applic 30, 1–19 (2018). https://doi.org/10.1007/s00521-016-2679-8

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