Using a Multi-view Convolutional Neural Network to monitor solar irradiance

Huertas‐Tato, Javier; Galván, Inés María; Aler, Ricardo; Rodríguez-Benítez, Francisco J.; Pozo-Vázquez, D.

doi:10.1007/s00521-021-05959-y

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…These criteria are determined by assessing the performance obtained with certain error metrics when considering different model alternatives. In the field of renewable energy research, DL models, especially CNNs, have been proven to be reliable tools for solar irradiance and PV power output prediction [64]- [66].…”

Section: Figure 5 Ann and DL Models Used In The Field Of Renewable En...mentioning

confidence: 99%

Influence of T6 heat Treatment on the Mechanical Properties of AA7075 and AA7020 alloys

Büyük¹,

Çadırlı²,

Kaya³

et al. 2023

Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century-Iii

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The AA7075 and AA7020 alloys were prepared using a vacuum melting furnace and a casting furnace. Mechanical properties of the alloy samples were investigated as-cast and under heat-treated conditions. To investigate the effect of heat treatment, numerous designed AA7075 and AA7020 samples were homogenized (solution process) in two steps (300 oC/12h+475 oC/12h) and then aged under different regimes. The mechanical properties of microhardness (HV), ultimate tensile strength (σUTS), tensile yield strength (σTYS), compressive yield strength (σCYS) and Young's modulus (E) were measured to investigate the effect of heat treatment on mechanical properties. Both alloy systems (AA7075 and AA7020) were compared both within themselves and with each other in terms of mechanical properties depending on how they were subjected to the heat treatment. Depending on the applied heat treatments, 7075 alloy exhibited superior mechanical properties compared to 7020 alloy. A good combination of high microhardness and reasonable tensile strength has been achieved for both alloy systems by sequential and appropriate heat treatment. While the peak sUTS value reached for the AA7075 alloy was 425 MPa (regime 3), it was determined as 320 MPa (regime 2) for the AA7020 alloy. After the aging process at 150 oC/24h (regime 2) applied proceeding the homogenization process, the peak HV and sCYS values for AA7020 alloy were achieved as 1235 MPa and 321.6 MPa, respectively.

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Section: Figure 5 Ann and DL Models Used In The Field Of Renewable En...mentioning

confidence: 99%

Influence of T6 heat Treatment on the Mechanical Properties of AA7075 and AA7020 alloys

Büyük¹,

Çadırlı²,

Kaya³

et al. 2023

Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century-Iii

View full text Add to dashboard Cite

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“…Huertas-Tato et al [ 6 ] present a multi-view convolutional neural network architecture to estimate solar irradiance from ground-level Total Sky Images (TSIs). Pairing Total Sky Images and Convolutional Neural Networks can effectively estimate Global Horizontal Irradiance (GHI) in photovoltaic (PV), replacing expensive equipment with off-the-shelf cameras.…”

Section: Summary Of Contributionsmentioning

confidence: 99%

“… Health (4 papers): Amor et al [ 3 ] (breast cancer, DNA methylation, deep embedded refined clustering), Nogueira-Rodríguez et al [ 11 ] (colorectal cancer, polyp detection, YOLOv3 architecture), Pérez and Ventura [ 14 ] (melanoma diagnosis, lesion segmentation, ensemble learning, genetic algorithm); Qureshi et al [ 15 ] (cardiovascular, healthcare systems, sensors, wearable technologies). Image and audio processing (6 papers): Fenza et al [ 5 ] (graph neural networks, name–face association, multimedia content), Huertas-Tato et al [ 6 ] (multi-view image, solar irradiance, Total Sky Images), Tarasiuk and Szczepaniak [ 18 ] (geometric transformations, invariance to rotation and scale CNN), Rodriguez-Conde et al [ 16 ] (object detection, on-device machine learning), Leroux et al [ 8 ] (storage requirements, residual networks, adaptive computation, resource-constrained deep learning), Li et al [ 9 ] (discrimination, Softmax loss, features discrimination, margin constraints). Industry (2 papers): Sierra-Garcia and Santos [ 17 ] (wind turbines, fuzzy control, pitch control, forecasting), Torres et al [ 19 ] (electricity demand, LSTM, time series forecasting).…”

Section: Introductionmentioning

confidence: 99%

“…Image and audio processing (6 papers): Fenza et al [ 5 ] (graph neural networks, name–face association, multimedia content), Huertas-Tato et al [ 6 ] (multi-view image, solar irradiance, Total Sky Images), Tarasiuk and Szczepaniak [ 18 ] (geometric transformations, invariance to rotation and scale CNN), Rodriguez-Conde et al [ 16 ] (object detection, on-device machine learning), Leroux et al [ 8 ] (storage requirements, residual networks, adaptive computation, resource-constrained deep learning), Li et al [ 9 ] (discrimination, Softmax loss, features discrimination, margin constraints).…”

Section: Introductionmentioning

confidence: 99%

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Recent advances on effective and efficient deep learning-based solutions

Martin

Camacho

2022

Neural Comput & Applic

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This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions.

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“…At present, the ANN models for solar irradiance prediction mainly focus on feedforward neural network (FNN). Various FNNs have been applied to predict solar irradiance, such as radial basis function (RBF) neural network [14], backpropagation (BP) neural network [15], convolutional neural network (CNN) [16]- [18] and so forth.…”

mentioning

confidence: 99%

Multi-Scale Convolutional Echo State Network With an Effective Pre-Training Strategy for Solar Irradiance Forecasting

Yang,

Li,

Guo

et al. 2024

IEEE Access

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In this article, a new kind of neural network model named multi-scale convolutional echo state network (MCESN) is proposed for solar irradiance prediction, which integrates the strong feature extraction capability of convolutional neural network (CNN) and the fast yet efficient prediction ability of echo state network (ESN). Firstly, the feature information at different time scales of solar irradiance (one dimensional series) data are extracted and selected by multi-scale CNN (MCNN) in the pre-training stage. Then, the trained features extracted above are concatenated and passed to ESN module as the input signal, which can be further encoded into high-dimensional state space; Meanwhile, the target solar irradiance value is fitted and predicted by ESN in the prediction phase. Finally, the effectiveness of MCESN is evaluated by hourly solar irradiance prediction. In experiment, RMSE, MAE, MAPE and R are chosen as four metrics to evaluate the performance of the proposed model. Simulation results demonstrate that the proposed MCESN perform better than classical ESN, MCNN, backpropagation (BP) random forest (RF), long short time memory (LSTM) and deep ESN (DESN) algorithms. INDEX TERMS Echo state network, multi-scale convolution, pre-processing, solar irradiance prediction. I. INTRODUCTION 1 In recent years, solar irradiance prediction plays signifi-2 cant roles in renewable energy systems. Solar energy is a 3 sustainable, environmental friendly, and, more importantly, 4 inexhaustible resource, and its potential cannot be matched 5 by other energy sources [1]. However, solar energy presents 6 highly nonlinear, naturally volatile and intermittent [2], due 7 to the fluidity of cloud and other variable meteorological 8 factors [3]. Undoubtedly, the uncertainty will have a certain 9 impact on solar energy systems [4]. Consequently, it is of 10 importance and urgency to predict solar energy accurately, 11 to ensure the security, efficiency and reliability of renewable 12 energy systems [5]. 13 Various techniques have been successfully proposed for 14 solar irradiance prediction, which can be roughly divided into 15 three categories: statistical models [6], [7], machine learning 16 algorithms [8]-[11] and artificial neural networks [12], [13]. 17 Among these techniques, artificial neural networks (ANN) 18 have become the most mainstream methods, because of their 19 strong nonlinear fitting ability and accurate prediction results. 20 This article has been accepted for publication in IEEE Access.

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Using a Multi-view Convolutional Neural Network to monitor solar irradiance

Cited by 9 publications

References 28 publications

Influence of T6 heat Treatment on the Mechanical Properties of AA7075 and AA7020 alloys

Influence of T6 heat Treatment on the Mechanical Properties of AA7075 and AA7020 alloys

Recent advances on effective and efficient deep learning-based solutions

Multi-Scale Convolutional Echo State Network With an Effective Pre-Training Strategy for Solar Irradiance Forecasting

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