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A stochastic sensitivity-based multi-objective optimization method for short-term wind speed interval prediction

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Abstract

With the increasing penetration of wind power in renewable energy systems, it is important to improve the accuracy of wind speed prediction. However, wind power generation has great uncertainties which make high-quality interval prediction a challenge. Existing multi-objective optimization interval prediction methods do not consider the robustness of the model. Thus, trained models for wind speed interval prediction may not be optimal for future predictions. In this paper, the prediction interval coverage probability, the prediction interval average width, and the robustness of the model are used as three objective functions for determining the optimal model of short-term wind speed interval prediction using multi-objective optimization. Furthermore, a new Stochastic Sensitivity for Prediction Intervals (SS_PIs) is proposed in this work to measure the stability and robustness of the model for interval prediction. Using wind farm data from countries on two different continents as case studies, experimental results show that the proposed method yields better prediction intervals in terms of all metrics including prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW) and SS_PIs. For example, at the prediction interval nominal confidence (PINC) of 85%, 90% and 95%, the proposed method has the best performance in all metrics of the USA wind farm dataset.

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Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

ARIMA:

Autoregressive integrated moving average model

ARMA:

Autoregressive moving average model

BP:

Back propagation

EEMD:

Electromagnetic mode decomposition

EMD:

Empirical mode decomposition

GRU:

Gated recurrent unit

ICEEMDAN:

Improved ceemdan

LSTM:

Long short-term memory

LUBE:

Lower upper bound estimation

MLP:

Multilayer perceptron

MOGA:

Multi-objective genetic algorithm

MSE:

Mean square error

NSGA-III:

Non-dominated sorting genetic algorithm-III

NWP:

Numerical weather prediction

PICP:

Prediction interval coverage probability

PINAW:

Prediction interval normalized average width

PINC:

Prediction interval nominal confidence

PIs:

Prediction intervals

RNN:

Recurrent neural network

SS_PIs:

Stochastic sensitivity for prediction intervals

SSMOO:

Stochastic sensitivity-based multi-objective optimization

WNN-PICEA-g:

Wavelet neural network—preference inspired co-evolutionary algorithm—goal vectors

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Acknowledgements

This work is in part supported by National Natural Science Foundation of China under Grants 61876066 and 61572201, in part by Guangdong Province Scienceand Technology Plan Project (Collaborative Innovation and Platform Environment Construction) 2019A050510006, in part by Brunel University London BRIEF Funding, UK and in part by the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022].

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Authors and Affiliations

  1. Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510630, China

    Xuanqun Chen, Wing W. Y. Ng & Cankun Zhong

  2. Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Chun Sing Lai, Keda Pan & Loi Lei Lai

  3. Brunel Interdisciplinary Power Systems Research Centre, Brunel University London, London, UB8 3PH, UK

    Chun Sing Lai

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  1. Xuanqun Chen
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  5. Loi Lei Lai
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Correspondence to Chun Sing Lai, Loi Lei Lai or Cankun Zhong.

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Chen, X., Lai, C.S., Ng, W.W.Y. et al. A stochastic sensitivity-based multi-objective optimization method for short-term wind speed interval prediction. Int. J. Mach. Learn. & Cyber. 12, 2579–2590 (2021). https://doi.org/10.1007/s13042-021-01340-6

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  • DOI: https://doi.org/10.1007/s13042-021-01340-6

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