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Iterative multi-task learning for time-series modeling of solar panel PV outputs

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  • Shireen, Tahasin
  • Shao, Chenhui
  • Wang, Hui
  • Li, Jingjing
  • Zhang, Xi
  • Li, Mingyang

Abstract

Time-series modeling of PV output for solar panels can help solar panel owners understand the power systems’ time-varying behavior and be prepared for the load demand. The time-series forecast/prediction can become challenging due to many missing observations or a lack of historical records that are not sufficient to establish statistical models. Increasing PV measurement frequency over a longer period increases the cost in the detection of the PV fluctuation. This paper proposes an efficient approach to iterative multi-task learning for time series (MTL-GP-TS) that improves prediction of the PV output without increasing measurement efforts by sharing the information among PV data from multiple similar solar panels. The proposed iterative MTL-GP-TS model learns/imputes unobserved or missing values in a dataset of time series associated with the solar panel of interest to predict the PV trend. Additionally, the method improves and generalizes the traditional multi-task learning for Gaussian Process to the learning of both global trend and local irregular components in time series. A real-world case study demonstrated that the proposed method could result in substantial improvement of predictions over conventional approaches. The paper also discusses the selection of parameters and data sources when implementing the proposed algorithm.

Suggested Citation

  • Shireen, Tahasin & Shao, Chenhui & Wang, Hui & Li, Jingjing & Zhang, Xi & Li, Mingyang, 2018. "Iterative multi-task learning for time-series modeling of solar panel PV outputs," Applied Energy, Elsevier, vol. 212(C), pages 654-662.
    • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:654-662
    DOI: 10.1016/j.apenergy.2017.12.058
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    Cited by:

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    3. Wang, Jing-Yi & Qian, Zheng & Zareipour, Hamidreza & Wood, David, 2018. "Performance assessment of photovoltaic modules based on daily energy generation estimation," Energy, Elsevier, vol. 165(PB), pages 1160-1172.
    4. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    5. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    6. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    7. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
    8. Miguel López Santos & Xela García-Santiago & Fernando Echevarría Camarero & Gonzalo Blázquez Gil & Pablo Carrasco Ortega, 2022. "Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting," Energies, MDPI, vol. 15(14), pages 1-22, July.
    9. Yang, Mao & Zhao, Meng & Huang, Dawei & Su, Xin, 2022. "A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder," Renewable Energy, Elsevier, vol. 194(C), pages 659-673.
    10. Qu, Jiaqi & Qian, Zheng & Pei, Yan & Wei, Lu & Zareipour, Hamidreza & Sun, Qiang, 2022. "An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection," Applied Energy, Elsevier, vol. 319(C).
    11. Gilanifar, Mostafa & Parvania, Masood, 2021. "Clustered multi-node learning of electric vehicle charging flexibility," Applied Energy, Elsevier, vol. 282(PB).
    12. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
    13. Musaed Alhussein & Syed Irtaza Haider & Khursheed Aurangzeb, 2019. "Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance," Energies, MDPI, vol. 12(8), pages 1-27, April.

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