EMD-FFT-SARIMA photovoltaic power generation prediction model using fast fourier transform optimization cycle parameters

Xiong Chuanyu; Liao Xiaohong; He Shiying; Chen Ran; Wang Wei; Zang Nan; Wang Ying; Xiao Menghan

doi:10.11884/HPLPB202436.230349

Volume 36 Issue 8

Jul. 2024

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Xiong Chuanyu, Liao Xiaohong, He Shiying, et al. EMD-FFT-SARIMA photovoltaic power generation prediction model using fast fourier transform optimization cycle parameters[J]. High Power Laser and Particle Beams, 2024, 36: 085001. doi: 10.11884/HPLPB202436.230349

Citation:

Xiong Chuanyu, Liao Xiaohong, He Shiying, et al. EMD-FFT-SARIMA photovoltaic power generation prediction model using fast fourier transform optimization cycle parameters[J]. High Power Laser and Particle Beams, 2024, 36: 085001. doi: 10.11884/HPLPB202436.230349

Citation:

Xiong Chuanyu, Liao Xiaohong, He Shiying, et al. EMD-FFT-SARIMA photovoltaic power generation prediction model using fast fourier transform optimization cycle parameters[J]. High Power Laser and Particle Beams, 2024, 36: 085001. doi: 10.11884/HPLPB202436.230349

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EMD-FFT-SARIMA photovoltaic power generation prediction model using fast fourier transform optimization cycle parameters

doi: 10.11884/HPLPB202436.230349

Xiong Chuanyu^1
,,
Liao Xiaohong¹,
He Shiying^{2
,
,},
Chen Ran¹,
Wang Wei¹,
Zang Nan^{2, 3},
Wang Ying^{2, 4},
Xiao Menghan^{5, 6}

1.
Economic and Technological Research Institute, State Grid Hubei Electric Power Co., Ltd. Wuhan 430000, China
2.
Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
3.
School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, China
4.
Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
5.
School of Electrical Engineering and Electronics, Huazhong University of Science and Technology, Wuhan 430074, China
6.
Electric Power Research Institute, State Grid Hubei Electric Power Co., Ltd. Wuhan 430000, China

Received Date: 2023-10-11
Accepted Date: 2024-03-11
Rev Recd Date: 2024-03-11

Available Online: 2024-05-27

Publish Date: 2024-07-04

Abstract

Abstract

In this paper, the photovoltaic (PV) power prediction model is optimized according to the characteristics of PV output units in distributed energy industrial parks to provide data support for the subsequent dispatching strategy. The EMD-SARIMA forecasting model is a combination of Empirical Mode Decomposition (EMD) and Seasonal Autoregressive Integrated Moving Average (SARIMA). In the model, the problem of determining the period of each IMF component of the signal component is proposed, the period T calculation method incorporating fast Fourier transform (FFT) is proposed, and the obtained period is fed into SARIMA as an input parameter together with the IMF sequence for prediction, which constitutes the EMD-FFT-SARIMA prediction model. Then, the prediction results corresponding to each IMF are superimposed and reconstructed to obtain the final prediction results. The error calculation of the prediction results reveals that the root mean square error (RMSE) decreases from 120.6 MW to 19.3 MW, and the mean absolute error (MAE) decreases from 52.87 MW to 12.3 MW.

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References(14)

References

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