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Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model

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  • Wang, Lining
  • Mao, Mingxuan
  • Xie, Jili
  • Liao, Zheng
  • Zhang, Hao
  • Li, Huanxin

Abstract

The stability operation and real-time control of the integrated energy system with distributed energy resources determines the higher and higher requirements for the accuracy of solar photovoltaic (PV) output power prediction. This paper proposes an accurate PV power prediction interval approach based on frequency-domain decomposition and hybrid deep learning (DL) model. In the proposed approach, ensemble empirical mode decomposition (EEMD) is firstly used to decompose and reconstruct the original PV energy time-series data into high and low-frequency sub-series followed by the statistical feature extraction process. Furthermore, an improved long-short-term-memory network (LSTM) model with the designed hyperparameters based on Bayesian optimization (BO) is proposed to predict the sub-series with the different minute-hour-day intervals. Moreover, support vector regression (SVR) is utilized to analyze the initial time node and reduce the fluctuation error of the prediction value near zero. Finally, a comparative study with SVR, KNN, BPNN, GRU, Stacked-LSTM, LSTM, LSTM-SVR, and LSTM-SVR-BO models is constructed by using an actual dataset collected from Arizona, US. The simulation results on the datasets show the proposed prediction model outperforms the other 7 models for PV power forecasting in 1 day, 7 days, and 14 days ahead prediction with the different minute-hour-day intervals. Especially, in the seven days ahead prediction case, the proposed model's average RMSE and AbsDEV values are as low as 4.157 and 0.116, where the prediction accuracy and prediction stability are improved by about 15% on average compared to the other prediction models.

Suggested Citation

  • Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222024781
    DOI: 10.1016/j.energy.2022.125592
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    Cited by:

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    2. Dai, Yeming & Yu, Weijie & Leng, Mingming, 2024. "A hybrid ensemble optimized BiGRU method for short-term photovoltaic generation forecasting," Energy, Elsevier, vol. 299(C).
    3. Zhou, Kaile & Chu, Yibo & Hu, Rong, 2023. "Energy supply-demand interaction model integrating uncertainty forecasting and peer-to-peer energy trading," Energy, Elsevier, vol. 285(C).
    4. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    5. Rosen, Karol & Angeles-Camacho, César & Elvira, Víctor & Guillén-Burguete, Servio Tulio, 2023. "Intra-hour photovoltaic forecasting through a time-varying Markov switching model," Energy, Elsevier, vol. 278(PB).
    6. Jizhong Xue & Zaohui Kang & Chun Sing Lai & Yu Wang & Fangyuan Xu & Haoliang Yuan, 2023. "Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)," Energies, MDPI, vol. 16(11), pages 1-18, May.
    7. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.

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