Skip to main content
Top
Published in:

20-03-2023 | Original Paper

An innovative power prediction method for bifacial PV modules

Authors: Li Yunqiao, Feng Yan

Published in: Electrical Engineering | Issue 4/2023

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The increasing proportion of bifacial photovoltaic modules (Bi-PVM) in new projects makes the operation of photovoltaic system (PVS) more complicated, and it is difficult to accurately predict the power of the PVS. To solve this problem, this paper proposes a new power prediction method for PVS based on Bi-PVM. Firstly, the equal proportion digital twin model of the example project is constructed. The superposition principle is used to analyze the factors affecting the power generation performance of Bi-PVM, and the characteristic project is constructed according to the analysis results. Secondly, the bifacial correction coefficient is introduced to reduce the parameter error caused by Bi-PVM to the prediction model. On this basis, a power prediction machine learning model based on bidirectional gated recurrent unit (Bi-GRU) network is established. Finally, a simulation experiment is carried out on the TensorFlow machine learning platform. With the actual operation data of a PV power station in Jiuquan, China, the simulation analysis is carried out under four weather types, namely, sunny day, rainy day, snowy day and complex and changeable day, respectively, which verified the correctness and excellence of the proposed method.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
2.
go back to reference Jiarong R, Li Y, Zhang H (2018) Multi-sensor fault detection and positioning method of photovoltaic array based on improved BP neural network. J Solar Energy 39(01):110–116 Jiarong R, Li Y, Zhang H (2018) Multi-sensor fault detection and positioning method of photovoltaic array based on improved BP neural network. J Solar Energy 39(01):110–116
3.
go back to reference Massaoudi M, Chihi I, Abu-Rub H, Refaat SS, Oueslati FS (2021) Convergence of photovoltaic power forecasting and deep learning: state-of-art review. IEEE Access 9:136593–136615CrossRef Massaoudi M, Chihi I, Abu-Rub H, Refaat SS, Oueslati FS (2021) Convergence of photovoltaic power forecasting and deep learning: state-of-art review. IEEE Access 9:136593–136615CrossRef
4.
go back to reference Hossain MS, Mahmood H (2020) Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast. IEEE Access 8:172524–172533CrossRef Hossain MS, Mahmood H (2020) Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast. IEEE Access 8:172524–172533CrossRef
5.
go back to reference Keerthisinghe C, Mickelson E, Kirschen DS, Shih N, Gibson S (2020) Improved PV forecasts for capacity firming. IEEE Access 8:152173–152182CrossRef Keerthisinghe C, Mickelson E, Kirschen DS, Shih N, Gibson S (2020) Improved PV forecasts for capacity firming. IEEE Access 8:152173–152182CrossRef
6.
go back to reference Sundararajan A, Ollis B (2021) Regression and generalized additive model to enhance the performance of photovoltaic power ensemble predictors. IEEE Access 9:111899–111914CrossRef Sundararajan A, Ollis B (2021) Regression and generalized additive model to enhance the performance of photovoltaic power ensemble predictors. IEEE Access 9:111899–111914CrossRef
7.
go back to reference Eom H, Son Y, Choi S (2020) Feature-selective ensemble learning-based long-term regional PV generation forecasting. IEEE Access 8:54620–54630CrossRef Eom H, Son Y, Choi S (2020) Feature-selective ensemble learning-based long-term regional PV generation forecasting. IEEE Access 8:54620–54630CrossRef
8.
go back to reference Ding S, Li R, Tao Z (2021) A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting. Energy Convers Manage 227:113644CrossRef Ding S, Li R, Tao Z (2021) A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting. Energy Convers Manage 227:113644CrossRef
9.
go back to reference Ray B, Shah R, Islam MR, Islam S (2020) A new data driven long-term solar yield analysis model of photovoltaic power plants. IEEE Access 8:136223–136233CrossRef Ray B, Shah R, Islam MR, Islam S (2020) A new data driven long-term solar yield analysis model of photovoltaic power plants. IEEE Access 8:136223–136233CrossRef
10.
go back to reference Kuo WC, Chen CH, Chen SY, Wang CC (2022) Deep learning neural networks for short-term PV Power Forecasting via Sky Image method. Energies 15(13):4779CrossRef Kuo WC, Chen CH, Chen SY, Wang CC (2022) Deep learning neural networks for short-term PV Power Forecasting via Sky Image method. Energies 15(13):4779CrossRef
11.
go back to reference Wang X, Sun Y, Luo D, Peng J (2022) Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification. Energy 240:122733CrossRef Wang X, Sun Y, Luo D, Peng J (2022) Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification. Energy 240:122733CrossRef
12.
go back to reference Netsanet S, Zheng D, Zhang W, Teshager G (2022) Short-term PV power forecasting using variational mode decomposition integrated with Ant colony optimization and neural network. Energy Rep 8:2022–2035CrossRef Netsanet S, Zheng D, Zhang W, Teshager G (2022) Short-term PV power forecasting using variational mode decomposition integrated with Ant colony optimization and neural network. Energy Rep 8:2022–2035CrossRef
13.
go back to reference Lateko AA, Yang HT, Huang CM (2022) Short-term pv power forecasting using a regression-based ensemble method. Energies 15(11):4171CrossRef Lateko AA, Yang HT, Huang CM (2022) Short-term pv power forecasting using a regression-based ensemble method. Energies 15(11):4171CrossRef
14.
go back to reference Serrano Ardila VM, Maciel JN, Ledesma JJG, Ando Junior OH (2022) Fuzzy time series methods applied to (In) direct short-term photovoltaic power forecasting. Energies 15(3):845CrossRef Serrano Ardila VM, Maciel JN, Ledesma JJG, Ando Junior OH (2022) Fuzzy time series methods applied to (In) direct short-term photovoltaic power forecasting. Energies 15(3):845CrossRef
15.
go back to reference Wangss Y, Yang Q, Xue H, Mi Y, Tu Y (2022) Ultra-short-term PV power prediction model based on HP-OVMD and enhanced emotional neural network. IET Renew Power Gener 16(11):2233–2247CrossRef Wangss Y, Yang Q, Xue H, Mi Y, Tu Y (2022) Ultra-short-term PV power prediction model based on HP-OVMD and enhanced emotional neural network. IET Renew Power Gener 16(11):2233–2247CrossRef
16.
go back to reference Chen X, Ding K, Zhang J, Han W, Liu Y, Yang Z, Weng S (2022) Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM. Energy 248:123574CrossRef Chen X, Ding K, Zhang J, Han W, Liu Y, Yang Z, Weng S (2022) Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM. Energy 248:123574CrossRef
17.
go back to reference Wang F, Lu X, Mei S, Su Y, Zhen Z, Zou Z, Catalão JP (2022) A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant. Energy 238:121946CrossRef Wang F, Lu X, Mei S, Su Y, Zhen Z, Zou Z, Catalão JP (2022) A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant. Energy 238:121946CrossRef
18.
go back to reference Samer RAB, Ismail BB, Abdullah AZ, Ali IM (2021) Simulation analysis of a 3.37 MW PV system using bifacial modules in desert environment. In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1878(1): p. 012026 Samer RAB, Ismail BB, Abdullah AZ, Ali IM (2021) Simulation analysis of a 3.37 MW PV system using bifacial modules in desert environment. In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1878(1): p. 012026
19.
go back to reference Bhang BG, Lee W, Kim GG, Choi JH, Park SY, Ahn HK (2019) Power performance of bifacial c-Si PV modules with different shading ratios. IEEE J Photovolt 9(5):1413–1420CrossRef Bhang BG, Lee W, Kim GG, Choi JH, Park SY, Ahn HK (2019) Power performance of bifacial c-Si PV modules with different shading ratios. IEEE J Photovolt 9(5):1413–1420CrossRef
20.
go back to reference IEC (2021) Terrestrial photovoltaic (PV) modules, IEC Standard 61215 IEC (2021) Terrestrial photovoltaic (PV) modules, IEC Standard 61215
21.
go back to reference Xu FY, Tang RX, Xu SB, Fan YL, Zhou Y, Zhang HT (2021) Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification. Energy 223:119748CrossRef Xu FY, Tang RX, Xu SB, Fan YL, Zhou Y, Zhang HT (2021) Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification. Energy 223:119748CrossRef
22.
go back to reference Nie Y, Zamzam AS, Brandt A (2021) Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks. Sol Energy 224:341–354CrossRef Nie Y, Zamzam AS, Brandt A (2021) Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks. Sol Energy 224:341–354CrossRef
23.
go back to reference Zhou Y, Zhou N, Gong L, Jiang M (2020) Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine. Energy 204:117894CrossRef Zhou Y, Zhou N, Gong L, Jiang M (2020) Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine. Energy 204:117894CrossRef
24.
go back to reference Pal S, Reinders A, Saive R (2020) Simulation of bifacial and monofacial silicon solar cell short-circuit current density under measured spectro-angular solar irradiance. IEEE J Photovolt 10(6):1803–1815CrossRef Pal S, Reinders A, Saive R (2020) Simulation of bifacial and monofacial silicon solar cell short-circuit current density under measured spectro-angular solar irradiance. IEEE J Photovolt 10(6):1803–1815CrossRef
25.
go back to reference Ghenai C, Ahmad FF, Rejeb O, Hamid AK (2021) Sensitivity analysis of design parameters and power gain correlations of bi-facial solar PV system using response surface methodology. Sol Energy 223:44–53CrossRef Ghenai C, Ahmad FF, Rejeb O, Hamid AK (2021) Sensitivity analysis of design parameters and power gain correlations of bi-facial solar PV system using response surface methodology. Sol Energy 223:44–53CrossRef
26.
go back to reference Massaoudi M, Chihi I, Sidhom L, Trabelsi M, Refaat SS, Abu-Rub H, Oueslati FS (2021) An effective hybrid NARX-LSTM model for point and interval PV power forecasting. IEEE Access 9:36571–36588CrossRef Massaoudi M, Chihi I, Sidhom L, Trabelsi M, Refaat SS, Abu-Rub H, Oueslati FS (2021) An effective hybrid NARX-LSTM model for point and interval PV power forecasting. IEEE Access 9:36571–36588CrossRef
27.
go back to reference Kopecek R, Libal J (2021) Bifacial photovoltaics 2021: Status, opportunities and challenges. Energies 14(8):2076CrossRef Kopecek R, Libal J (2021) Bifacial photovoltaics 2021: Status, opportunities and challenges. Energies 14(8):2076CrossRef
28.
go back to reference Ballakur AA, Arya A (2022) Empirical evaluation of gated recurrent neural network architectures in aviation delay prediction. In: 2020 5th international conference on computing, communication and security (ICCCS), IEEE, pp. 1–7 Ballakur AA, Arya A (2022) Empirical evaluation of gated recurrent neural network architectures in aviation delay prediction. In: 2020 5th international conference on computing, communication and security (ICCCS), IEEE, pp. 1–7
Metadata
Title
An innovative power prediction method for bifacial PV modules
Authors
Li Yunqiao
Feng Yan
Publication date
20-03-2023
Publisher
Springer Berlin Heidelberg
Published in
Electrical Engineering / Issue 4/2023
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-023-01805-7

Other articles of this Issue 4/2023

Electrical Engineering 4/2023 Go to the issue