Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction

Ghimire, SujanDeo, Ravinesh C.Casillas-Pérez, DavidSalcedo-Sanz, SanchoSharma, EktaAli, Mumtaz2023-09-252023-09-252022Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez, Sancho Salcedo-Sanz, Ekta Sharma, Mumtaz Ali, Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction, Measurement, Volume 202, 2022, 111759, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2022.1117590263-2241https://hdl.handle.net/10115/24515We greatly acknowledge these for the data (i) Queensland Climate Change Centre of Excellence (QCCCE), a part of the Department of Science, Information Technology, Innovation, and the Arts (DSITIA) (ii) the Centre for Environmental Data Analysis (CEDA) as a server for the CMIP5 project’s GCM output collection for CSIRO-BOM ACCESS1-0, MOHC Hadley-GEM2-CC and the MRI MRI-CGCM3. Partial support of this study is through the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN) .Global solar radiation (GSR) prediction plays an essential role in planning, controlling and monitoring solar power systems. However, its stochastic behaviour is a significant challenge in achieving satisfactory prediction results. This study aims to design an innovative hybrid prediction model that integrates a feature selection mechanism using a Slime-Mould algorithm, a Convolutional-Neural-Network (CNN), a Long– Short-Term-Memory Neural Network (LSTM) and a final CNN with Multilayer-Perceptron output (SCLC algorithm hereafter). The proposed model was applied to six solar farms in Queensland (Australia) at daily temporal horizons in six different time steps. The comprehensive benchmarking of the obtained results with those from two Deep-Learning (CNN-LSTM, Deep-Neural-Network) and three Machine-Learning (ArtificialNeural-Network, Random-Forest, Self-Adaptive Differential-Evolutionary Extreme-Learning-Machines) models highlighted a higher performance of the proposed prediction model in all the six selected solar farms. From the results obtained, this work establishes that the designed SCLC algorithm could have a practical utility for applications in renewable and sustainable energy resource management.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Global solar predictionDeep Learning networksConvolutional Neural NetworksSlime Mould AlgorithmRenewable energyGlobal climate modelsDeep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation predictioninfo:eu-repo/semantics/article10.1016/j.measurement.2022.111759info:eu-repo/semantics/openAccess