Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms

Ghimire, Sujan; Deo, Ravinesh C.; Casillas-Pérez, David; Salcedo‐Sanz, Sancho

doi:10.1016/j.apenergy.2022.119063

Cited by 49 publications

(10 citation statements)

References 82 publications

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“…In comparison with different DL models (e.g., deep belief network (DBN), deep neural network, artificial neural network, etc. ), a DL hybrid model consisting of CNN, an extreme gradient boosting with RF, and a Harris Hawks Optimization, was more efficient for predicting boosting solar radiation 53 . The spatial map of soil salinity generated by a one-dimensional convolution neural network—long short-term memory (1DCNN-LSTM) DL hybrid model was more accurate than the salinity map produced by deep Boltzmann machine (DBM) DL individual model 54 .…”

Section: Resultsmentioning

confidence: 99%

Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source

Gholami

Mohammadifar

2022

Sci Rep

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Dust storms have many negative consequences, and affect all kinds of ecosystems, as well as climate and weather conditions. Therefore, classification of dust storm sources into different susceptibility categories can help us mitigate its negative effects. This study aimed to classify the susceptibility of dust sources in the Middle East (ME) by developing two novel deep learning (DL) hybrid models based on the convolutional neural network–gated recurrent unit (CNN-GRU) model, and the dense layer deep learning–random forest (DLDL-RF) model. The Dragonfly algorithm (DA) was used to identify the critical features controlling dust sources. Game theory was used for the interpretability of the DL model’s output. Predictive DL models were constructed by dividing datasets randomly into train (70%) and test (30%) groups, six statistical indicators being then applied to assess the DL hybrid model performance for both datasets (train and test). Among 13 potential features (or variables) controlling dust sources, seven variables were selected as important and six as non-important by DA, respectively. Based on the DLDL-RF hybrid model – a model with higher accuracy in comparison with CNN-GRU–23.1, 22.8, and 22.2% of the study area were classified as being of very low, low and moderate susceptibility, whereas 20.2 and 11.7% of the area were classified as representing high and very high susceptibility classes, respectively. Among seven important features selected by DA, clay content, silt content, and precipitation were identified as the three most important by game theory through permutation values. Overall, DL hybrid models were found to be efficient methods for prediction purposes on large spatial scales with no or incomplete datasets from ground-based measurements.

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Section: Resultsmentioning

confidence: 99%

Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source

Gholami

Mohammadifar

2022

Sci Rep

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“…A convolutional layer in CNN incorporates various convolution kernels for extracting different features. Convolutional and pooling layers combine to minimise parameters and accelerate computations (Ghimire et al, 2022). The fully connected layer then uses the convolution kernel's features to calculate the final prediction.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices

Ahmed

Farheen

Nguyen‐Huy

et al. 2023

Preprint

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This paper aims to propose a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) with a bi-directional long-short term memory (BiLSTM) for week-ahead prediction of daily flood index (IF) for Bangladesh. The neighbourhood component analysis (NCA) is assigned for significant feature selection with synoptic-scale climatic indicators. The results successfully reveal that the hybrid CNN-BiLSTM model outperforms the respective benchmark models based on forecasting capability, as supported by a minimal mean absolute error and high-efficiency metrics. With respect to IF prediction, the hybrid CNN-BiLSTM model shows over 98% of the prediction errors were less than 0.015, resulting in a low relative error and superiority performance against the benchmark models in this study. The adaptability and potential utility of the suggested model may be helpful in subsequent flood monitoring and may also be beneficial to policymakers at the federal and state levels.

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“…The optimization of each solar panel according to the time of day and its geographical location is a method to take into account the atmospheric variations in the design of solar cells to produce more energy. It includes in the calculation of solar power generation systems all the variations of the solar spectrum to predict the production of solar photovoltaic energy by: − Using a statistical and artificial intelligence technique called clustering [37]; − Boosting solar radiation predictions with global climate models, observational predictors, and hybrid deep-machine learning algorithms [38]; − Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms [39]; − Improving the efficacy of diagnosis and remote sensing of solar photovoltaic systems [40].…”

Section: Application Of Artificial Intelligence Against Overheating O...mentioning

confidence: 99%

Solar panels overheating protection: a review

Laabab

Ziani

Benami

2022

IJEECS

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Reduced energy consumption is a direct result of building optimization. Solar panels and heat detectors can provide the necessary power. After years of refinement and development, methods for regulating solar panels' output are finally finding their way into everyday life, where they are having a dramatic impact on the foundations of sustainability. This document provides an up-to-date assessment of several strategies for preventing solar panels from overheating, all of which serve to boost their efficiency and prolong their service life. It begins with a brief background on protection technologies before moving on to a survey of recently filed patent applications that address this topic. The study concludes with a proposed assessment of the strengths and shortcomings of these safeguards. We anticipate that the information included in this review paper will be an invaluable tool for scholars and researchers.

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Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms

Cited by 49 publications

References 82 publications

Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source

Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source

Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices

Solar panels overheating protection: a review

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