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A New Data Driven Long-Term Solar Yield Analysis Model of Photovoltaic Power Plants

Journal Article


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Abstract


  • Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth

    that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such

    as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital.

    Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting

    method. It has been identified that the hybrid approach may outperform the individual technique in

    minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed

    for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain

    the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations

    (e.g. North Queensland) are used to train the model. The model is developed using the combination of

    multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed

    hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random

    forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance.

    The proposed model could be useful for generation planning and reserve estimation in power systems with

    high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs).

UOW Authors


  •   Ray, Biplob (external author)
  •   Shah, Rakibuzzaman (external author)
  •   Islam, Md Rabiul
  •   Islam, Syed (external author)

Publication Date


  • 2020

Citation


  • B. Ray, R. Shah, M. Islam & S. Islam, "A New Data Driven Long-Term Solar Yield Analysis Model of Photovoltaic Power Plants," IEEE Access, vol. 8, pp. 136223-1-136223-11, 2020.

Scopus Eid


  • 2-s2.0-85089565038

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5323&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4295

Start Page


  • 136223-1

End Page


  • 136223-11

Volume


  • 8

Place Of Publication


  • United States

Abstract


  • Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth

    that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such

    as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital.

    Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting

    method. It has been identified that the hybrid approach may outperform the individual technique in

    minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed

    for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain

    the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations

    (e.g. North Queensland) are used to train the model. The model is developed using the combination of

    multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed

    hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random

    forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance.

    The proposed model could be useful for generation planning and reserve estimation in power systems with

    high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs).

UOW Authors


  •   Ray, Biplob (external author)
  •   Shah, Rakibuzzaman (external author)
  •   Islam, Md Rabiul
  •   Islam, Syed (external author)

Publication Date


  • 2020

Citation


  • B. Ray, R. Shah, M. Islam & S. Islam, "A New Data Driven Long-Term Solar Yield Analysis Model of Photovoltaic Power Plants," IEEE Access, vol. 8, pp. 136223-1-136223-11, 2020.

Scopus Eid


  • 2-s2.0-85089565038

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5323&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4295

Start Page


  • 136223-1

End Page


  • 136223-11

Volume


  • 8

Place Of Publication


  • United States