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Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction

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Abstract

Urgent transition from the dependence on fossil fuels towards renewable energies requires more solar photovoltaic power to be connected to the electricity grids, with reliable supply through accurate solar radiation forecasting systems. This study proposes an innovative hybrid method that integrates convolutional neural network (CNN) with multi-layer perceptron (MLP) to generate global solar radiation (GSR) forecasts. The CMLP model first extracts optimal topological and structural features embedded in predictive variables through a CNN-based feature extraction stage followed by an MLP-based predictive model to generate the GSR forecasts. Predictive variables from observed data and global climate models (GCM) are used to predict GSR at six solar farms in Queensland, Australia. A hybrid-wrapper feature selection method using a random forest-recursive feature elimination (RF-RFE) scheme is used to eradicate redundant predictor features to improve the proposed CMLP model efficiency. The CMLP model has been compared and bench-marked against seven artificial intelligence–based and seven temperature-based deterministic models, showing excellent performance at all solar energy study sites tested over daily, monthly, and seasonal scales. The proposed hybrid CMLP model should be explored as a viable modelling tool for solar energy monitoring and forecasting in real-time energy management systems.

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Availability of Data and Materials

All the data used in this paper can be freely downloaded from the following sources: (1) Scientific Information for Landowners (SILO) https://www.data.qld.gov.au/dataset/silo-climate-database; (2) Coupled Model Intercomparision Phase-5 global climate models (ACCESS1-0 from CSIRO-BOM, Hadley-GEM2-CC from MOHC, and MRI-CGCM3 from MRI).

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Acknowledgements

Data were acquired from global climate model archives and ground truth observation from the Scientific Information for Landowners (SILO) repository and Coupled Model Intercomparision Phase-5 global climate models (ACCESS1-0 from CSIRO-BOM, Hadley-GEM2-CC from MOHC, and MRI-CGCM3 from MRI) which are greatly acknowledged.

Funding

This research has been partially supported by Spanish Ministry of Science and Innovation (MICINN), through Project Number PID2020-115454GB-C21.

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Authors and Affiliations

  1. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, 4300, QLD, Australia

    Sujan Ghimire, Ravinesh C. Deo & Sancho Salcedo-Sanz

  2. SQNNSW Drought Resilience Adoption and Innovation Hub, University of Southern Queensland, Toowoomba, 4350, QLD, Australia

    Thong Nguyen-Huy

  3. Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, 4350, QLD, Australia

    Thong Nguyen-Huy

  4. Department of Science, School of Science and Technology, The University of Fiji, Saweni, Lautoka, Fiji

    Ramendra Prasad

  5. Department of Signal Processing and Communications, Universidad Rey Juan Carlos, Fuenlabrada, 28942, Madrid, Spain

    David Casillas-Pérez

  6. Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, Madrid, Spain

    Sancho Salcedo-Sanz

  7. Department of Railroad Engineering, Woosong University, Dongdaejeon-ro, 171, Daejeon, Republic of Korea

    Binayak Bhandari

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Correspondence to David Casillas-Pérez.

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Ghimire, S., Nguyen-Huy, T., Prasad, R. et al. Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction. Cogn Comput 15, 645–671 (2023). https://doi.org/10.1007/s12559-022-10070-y

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