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).