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Memory long and short term time series network for ultra-short-term photovoltaic power forecasting

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  • Huang, Congzhi
  • Yang, Mengyuan

Abstract

Photovoltaic (PV) power is stochastic, intermittent and volatile, which has brought huge challenges to the safe and stable operation of the power grid. Accurate PV power forecasting is becoming a significant task for PV plant grid connecting, scheduling and guaranteeing the safety of the power grid. To improve the accuracy of PV power forecasting, a memory long and short term time series network (MLSTNet) model is proposed to perform ultra-short-term PV power forecasting with a time horizon varying from 15 min to 4 h. Firstly, the input variables are screened by calculating Spearman correlation coefficients between weather data. To cluster different types of data, the classification coefficient is set. Secondly, different type data are automatically identified in the MLSTNet model. The appropriate model parameters and network structures are adjusted for the higher forecasting speed and accuracy. By using the single-step rolling input method and the temporal attention convolutional neural network, the ability of temporal and spatial feature extraction has been significantly enhanced, allowing real-time forecasting and dynamic optimization of the proposed model. Thirdly, long-term and short-term historical data are fed into the model, and different periods dependencies are captured while incorporating multiple variable dimensions, leading to more memorable in the network. Finally, the experimental data is selected from the actual operation data of a PV plant in northern China, which represents the universal applicability and practical application of the model. The performance of the proposed model is demonstrated with actual PV plant dataset, the RMSE of proposed model decreased by 32.73%, 5.44%, 12.44%, and 31.22%. Results indicated that it is feasible to use the proposed MLSTNet model for time series forecasting. The core idea of the MLSTNet model is to achieve higher accuracy in ultra-short-term forecasting by enhancing the ability to extract temporal features and linearity in the model, providing a new idea for deep learning-based methods for ultra-short-term PV power forecasting.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:279:y:2023:i:c:s0360544223013555
    DOI: 10.1016/j.energy.2023.127961
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    1. Pierro, Marco & Gentili, Damiano & Liolli, Fabio Romano & Cornaro, Cristina & Moser, David & Betti, Alessandro & Moschella, Michela & Collino, Elena & Ronzio, Dario & van der Meer, Dennis, 2022. "Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study," Renewable Energy, Elsevier, vol. 189(C), pages 983-996.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
    4. Liu, Yanli & Wang, Junyi, 2022. "Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 312(C).
    5. Limouni, Tariq & Yaagoubi, Reda & Bouziane, Khalid & Guissi, Khalid & Baali, El Houssain, 2023. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, Elsevier, vol. 205(C), pages 1010-1024.
    6. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    7. Wang, Fei & Lu, Xiaoxing & Mei, Shengwei & Su, Ying & Zhen, Zhao & Zou, Zubing & Zhang, Xuemin & Yin, Rui & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant," Energy, Elsevier, vol. 238(PC).
    8. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    9. Qu, Yinpeng & Xu, Jian & Sun, Yuanzhang & Liu, Dan, 2021. "A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting," Applied Energy, Elsevier, vol. 304(C).
    10. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    11. Rodríguez-Gallegos, Carlos D. & Vinayagam, Lokesh & Gandhi, Oktoviano & Yagli, Gokhan Mert & Alvarez-Alvarado, Manuel S. & Srinivasan, Dipti & Reindl, Thomas & Panda, S.K., 2021. "Novel forecast-based dispatch strategy optimization for PV hybrid systems in real time," Energy, Elsevier, vol. 222(C).
    12. Markovics, Dávid & Mayer, Martin János, 2022. "Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    13. Wang, Mengyuan & Xu, Xiaoyuan & Yan, Zheng, 2023. "Online fault diagnosis of PV array considering label errors based on distributionally robust logistic regression," Renewable Energy, Elsevier, vol. 203(C), pages 68-80.
    14. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
    15. Xiao, Xun & Mo, Huadong & Zhang, Yinan & Shan, Guangcun, 2022. "Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting," Energy, Elsevier, vol. 246(C).
    16. 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).
    17. Luo, Xing & Zhang, Dongxiao, 2023. "A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs," Energy, Elsevier, vol. 268(C).
    18. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
    19. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).
    20. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    21. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    22. Marc Jaxa-Rozen & Evelina Trutnevyte, 2021. "Sources of uncertainty in long-term global scenarios of solar photovoltaic technology," Nature Climate Change, Nature, vol. 11(3), pages 266-273, March.
    23. Ma, Huixin & Zhang, Chu & Peng, Tian & Nazir, Muhammad Shahzad & Li, Yiman, 2022. "An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting," Energy, Elsevier, vol. 256(C).
    24. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    25. Kushwaha, Vishal & Pindoriya, Naran M., 2019. "A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast," Renewable Energy, Elsevier, vol. 140(C), pages 124-139.
    26. Simeunović, Jelena & Schubnel, Baptiste & Alet, Pierre-Jean & Carrillo, Rafael E. & Frossard, Pascal, 2022. "Interpretable temporal-spatial graph attention network for multi-site PV power forecasting," Applied Energy, Elsevier, vol. 327(C).
    27. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
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    2. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    3. Wang, Shuangxin & Shi, Jiarong & Yang, Wei & Yin, Qingyan, 2024. "High and low frequency wind power prediction based on Transformer and BiGRU-Attention," Energy, Elsevier, vol. 288(C).
    4. Hui Wang & Su Yan & Danyang Ju & Nan Ma & Jun Fang & Song Wang & Haijun Li & Tianyu Zhang & Yipeng Xie & Jun Wang, 2023. "Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model," Sustainability, MDPI, vol. 15(21), pages 1-26, November.
    5. Liu, Jincheng & Li, Teng, 2024. "Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model," Energy, Elsevier, vol. 293(C).

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