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An integrated method with adaptive decomposition and machine learning for renewable energy power generation forecasting

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Abstract

In recent years, traditional energy sources have caused a variety of negative impacts on the environment, and reducing carbon emissions is a top priority. The development of renewable energy technology is the key to transform the energy structure. Renewable energy represented by wind energy and photovoltaics has abundant reserves so they are connected to the grid system on a large scale. However, because of natural energy’s randomness, renewable energy power generation poses potential risks to energy production and grid security. By making short-term forecasts of renewable energy generation power, the uncertainty of energy generation can be reduced, and it is crucial to study renewable energy forecasting techniques. This paper proposes an integrated forecasting system for renewable energy sources. Firstly, ensemble empirical mode decomposition is used for data preprocessing, and stationarity analysis is used for modal identification; then, support vector regression optimized by sparrow search algorithm and statistical methods are combined to make forecast according to different characteristics of the series respectively; finally, the feasibility of this method in renewable energy time series prediction is verified by experiments. The experiments prove that the proposed model effectively improves the accuracy and prediction performance on ultra-short-term renewable energy forecasting; and it has good applicability and competitiveness with different forecasting scenarios and characteristics, which satisfy the actual forecasting requirements in terms of operational efficiency and accuracy, thus providing a technical basis for the effective utilization of renewable energy.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ADF:

Augmented Dickey-Fuller test

ANN:

Artificial neural network

ARIMA:

Autoregressive Integrated Moving Average Model

BP:

Back propagation neural network

CEEMD:

Complementary ensemble empirical mode decomposition

CEEMDAN:

Complete ensemble empirical mode decomposition with adaptive noise

CNN:

Convolution neural network

EEMD:

Ensemble empirical mode decomposition

ELM:

Extreme learning machine

EMD:

Empirical mode decomposition

FCM:

Fuzzy C-means method

GA:

Genetic algorithm

IEA:

International Energy Agency

IMF:

Intrinsic mode function

LSSVM:

Least square SVM

LSTM:

Long and short-term memory neural network

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MSE:

Mean squared error

NWP:

Long and short-term memory neural network

PSO:

Particle swarm optimization algorithm

RBF:

Radial basis function neural network

RMSE:

Root mean squared error

SSA:

Singular spectrum analysis

SVM:

Support vector machine

SVR:

Support vector regression

TIC:

Theil inequality coefficient

VMD:

Variational mode decomposition

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Acknowledgements

The authors thank the Dr. Marcus Schulz and anonymous referees for the thoughtful and constructive suggestions that led to a considerable improvement of the paper.

Funding

This research was supported partly by the National Natural Science Foundation of China (U22B6006) and the S&T Program of Hebei (G2020502001).

Author information

Authors and Affiliations

  1. State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China

    Guomin Li, Leyi Yu, Ying Zhang, Peng Sun, Ruixuan Li, Yagang Zhang & Gengyin Li

  2. Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Box 205, Baoding, 071003, Hebei, China

    Leyi Yu, Ying Zhang, Peng Sun, Ruixuan Li & Yagang Zhang

  3. SGITG Accenture Information Technology Center Co., Ltd, Beijing, 100031, China

    Pengfei Wang

  4. Interdisciplinary Mathematics Institute, University of South Carolina, Columbia, SC, 29208, USA

    Yagang Zhang

Authors
  1. Guomin Li
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  2. Leyi Yu
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  3. Ying Zhang
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  4. Peng Sun
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  5. Ruixuan Li
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  6. Yagang Zhang
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  7. Gengyin Li
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  8. Pengfei Wang
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Contributions

Guomin Li: Conceptualization, Methodology, Software, Writing—Original Draft. Leyi Yu: Methodology, Visualization, Writing—Original Draft. Ying Zhang: Data Curation, Validation. Sun Peng: Writing—Review & Editing. Ruixuan Li: Methodology, Software. Yagang Zhang: Conceptualization, Supervision. Gengyin Li: Validation. Pengfei Wang: Data Curation.

Corresponding author

Correspondence to Yagang Zhang.

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Competing interests

The authors declare no competing interests.

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Responsible Editor: Marcus Schulz

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Li, G., Yu, L., Zhang, Y. et al. An integrated method with adaptive decomposition and machine learning for renewable energy power generation forecasting. Environ Sci Pollut Res 30, 41937–41953 (2023). https://doi.org/10.1007/s11356-023-25194-3

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  • DOI: https://doi.org/10.1007/s11356-023-25194-3

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