Machine learning for solar trackers

Carballo, Jose A.; Bonilla, Javier; Berenguel, Manuel; Fernández-Reche, Jesús; García, G. Martınez

doi:10.1063/1.5117524

Cited by 12 publications

(15 citation statements)

References 10 publications

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“…However, the 𝑚𝐴𝑃 has several shortcomings, some of which include: its subjectivity to the evaluation environment, failure to provide accurate values, and lack of explicit explanation. This could explain why Carballo et al [114] used 𝐼𝑜𝑈 as an overlap criterion (threshold) for the 𝑚𝐴𝑃. The 𝑚𝐴𝑃 was calculated based on fixed 𝐼𝑜𝑈 threshold, which meant that performance results higher than the threshold were treated equally.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, AL-Rousan et al [113] applied gradient descent with a momentum algorithm to train their model. Further, Carballo et al [114], RMSProp Optimizer was used to train the model. The authors of this study may have used the RMSProp Optimizer because it is an adaptive version of the SGD optimizer, which is able to adjust the excessive growth of the cumulative squares of gradients experienced with the SGD optimizer.…”

Section: Review Of Optimization Methodsmentioning

confidence: 99%

“…Table 3 shows a summary of the data type, data source and features used in the various publications.              [100]              [101]              [102]              [103]              [104]              [105]              [106]              [107]              [108]              [10]              [109]              [110]              [111]              [112]              [113]              [114]              [115]              [116]              [117]              [118]              [119]              [120]              [121]              [122]                           [125]              [126]              [127]              [128]              [129]              [130]              [131]              [132]             ...…”

Section: What Is the Nature Of Datasets Used In Solar Tracker-based D...unclassified

“…Further, the authors in [114,119,127], used 350 images based on an image set containing four object classes, namely sun, cloud, heliostat and target. The images were collected using embedded and mobile devices at the Center for Advanced Technologies solar field in Plataforma Solar de Almeria.…”

Section: What Is the Nature Of Datasets Used In Solar Tracker-based D...mentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning Techniques for Photovoltaic Solar Tracking Systems: A Systematic Literature Review

Phiri

Mulenga

Zimba

et al. 2023

Preprint

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Although photovoltaic (PV) panels are extensively used to convert solar energy into electric energy, the continuous change in the sun's angle with reference to the earth's surface limits their capacity to collect sufficient energy. To improve efficiency, solar trackers are used to constantly adjust the PV panels towards the sun to maximize energy capture. There has been an increase in the use of deep learning (DL) in solar tracking systems as it has proven to be one of the most efficient techniques owing to its success in related fields. Although there are several studies on different designs of solar tracking systems, a synthesis of the state-of-art knowledge is lacking in the literature. Therefore, this study carried out a review of the DL methods used in solar tracking systems. Specifically, dataset usage, preprocessing methods, feature engineering methods, DL algorithms and the performance metrics used in the identified studies. The review considered the studies published from 2012 to 2022. In the initial search, 5,724 articles were selected from 7 digital libraries. Only 37 academic papers were included in the review based on the inclusion criteria. The results revealed that deep hybrid learning models were the most popular among researchers. Further, the study identified research challenges and future directions relating to the availability of DL-based PV solar tracking with respect to datasets, image data, data normalization, data decomposition, and feature engineering methods. This work will be resourceful to current and future researchers in addressing the trends and challenges related to the application of DL in PV solar trackers.

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Section: Discussionmentioning

confidence: 99%

Section: Review Of Optimization Methodsmentioning

confidence: 99%

Section: What Is the Nature Of Datasets Used In Solar Tracker-based D...unclassified

Section: What Is the Nature Of Datasets Used In Solar Tracker-based D...mentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning Techniques for Photovoltaic Solar Tracking Systems: A Systematic Literature Review

Phiri

Mulenga

Zimba

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Generally, 75% of the data is utilized for training, and the remaining 25% for testing the model. Image-based machine learning [14] and reinforcement learning algorithms [15,16] have been applied to PV systems for MPPT. A converter is required to operate the PV panel at the MPP.…”

Section: Introductionmentioning

confidence: 99%

A New Multivariate Linear Regression MPPT Algorithm for Solar PV System with Boost Converter

Mahesh

Meyyappan

Alla³

2022

ECTI-EEC

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Operating solar photovoltaic (PV) panels at the maximum power point (MPP) is considered to enrich energy conversion efficiency. Each MPP tracking technique (MPPT) has its conversion efficiency and methodology for tracking the MPP. This paper introduces a new method for operating the PV panel at MPP by implementing the multivariate linear regression (MLR) machine learning algorithm. The MLR machine learning model in this study is trained and tested using the data collected from the PV panel specifications. This MLR algorithm can predict the maximum power available at the panel, and the voltage corresponds to this maximum power for specific values of irradiance and temperature. These predicted values help in the calculation of the duty ratio for the boost converter. The MATLAB/SIMULINK results illustrate that, as time progresses, the PV panel is forced to operate at the MPP predicted by the MLR algorithm, yielding a mean efficiency of more than 96% in the steady-state operation of the PV system, even under variable irradiances and temperatures.

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Modeling and Analysis of a Dynamic Smart Solar Panel System for Harvesting Solar Energy in Remote Areas

Mizanoor Rahman

2024

Proceedings of Ninth International Congress on Information and Communication Technology

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Machine learning for solar trackers

Cited by 12 publications

References 10 publications

Deep Learning Techniques for Photovoltaic Solar Tracking Systems: A Systematic Literature Review

Deep Learning Techniques for Photovoltaic Solar Tracking Systems: A Systematic Literature Review

A New Multivariate Linear Regression MPPT Algorithm for Solar PV System with Boost Converter

Modeling and Analysis of a Dynamic Smart Solar Panel System for Harvesting Solar Energy in Remote Areas

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