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A new data driven long-term solar yield analysis model of photovoltaic power plants

journal contribution
posted on 15.09.2020, 00:00 by Biplob Ray, Md Rakibuzzaman Shah, MR Islam, S Islam
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).

Funding

Other

History

Volume

8

Start Page

136223

End Page

136233

Number of Pages

11

eISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional Rights

CC BY 4.0

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

21/07/2020

External Author Affiliations

Federation University Australia; University of Wollongong

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

IEEE Access