Artificial Neural Network Technique to Predict The Sun Position in Cloudy State with Tetrahedron Based Sensor

Away, Yuwaldi; Syahrizal,; Walidainy, Hubbul; Rizal, Muhammad Syamsu; Novandri, Andri

doi:10.1109/cosite52651.2021.9649550

Cited by 3 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is incumbent upon assessments of solar tracking systems to comprehensively scrutinize these multifarious facets [11]. The tetrahedron sensor paradigm represents an innovative stride within the realm of solar sensors, leveraging the deployment of three pyramidal light-sensitive sensors [12,13]. The geometric allocation of these photometric sensors is elucidated in Figure 1.…”

Section: Mobile Sun Trackermentioning

confidence: 99%

Analysis of energy harvesting on 3-DOF mobile tetrahedron-based sun tracker

Away,

Novandri,

Rizal

2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

The use of solar panels to be placed on mobile platforms is still not able to absorb maximum solar energy. Therefore, we need a sun tracker system that is able to respond to disturbances from above, such as the movement of clouds, and also disturbances from below, such as movements from mobile platforms. The proposed mobile tetrahedron-based sun tracker is capable of fast and continuous response to movement and movement from various directions, with an effective control algorithm that will be able to keep the solar panels always perpendicular to the sun’s rays. This mobile tetrahedron-based sun tracker is a 3-DOF parallel construction with a stepper motor as the actuator. This sun tracker is installed on a moving platform and is capable of tracking the sun in a 3-DOF parallel manner. Test results with PV polycrystalline silicon with normal operating specifications of 5V/2W, show an average voltage measurement of around 2.62V and an average current of around 190.613mA. The average power produced is around 0.798W, with peak power reached around 11:40-12:00, while the lowest power was recorded at 07:00-08:00 and 17:00-18:00. The research results also obtained that the total electrical energy collected for one full day using a sun tracker was 9.571Wh (higher 7.166% than fixed PV).

show abstract

Section: Mobile Sun Trackermentioning

confidence: 99%

Analysis of energy harvesting on 3-DOF mobile tetrahedron-based sun tracker

Away,

Novandri,

Rizal

2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…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

“…Similarly, Shenawy et al, [100], used solar radiation, windspeed and energy performance data which was randomly generated using a function built in MATLAB Simulink software. Away et al [101] used azimuth and elevation angles and LDR data to train an ANN-based tetrahedron sensor to track the position of the sun. Further, while the works in [104,111,115,121,128] used LDR data, the authors in [118,120,122,130] used astronomical data to their proposed models.…”

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

confidence: 99%

“…On the other hand, Frizzo Stefenon et al [111] evaluated the performance of different variations of a DHL-based model using 𝑀𝐴𝐸, 𝑀𝐴𝑃𝐸, 𝑀𝑆𝐸, 𝑁𝑅𝑀𝑆𝐸, and 𝑅 2 . Similarly, Away et al [101], used 𝑀𝑆𝐸, 𝑀𝐴𝐸, and 𝑅 as error metrics to measure an ANN-based model's predictive performance. In contrast, a confusion matrix was used to measure the accuracy and precision of the model.…”

Section: Which Performance Metrics Are Used To Evaluate Solar Trackin...mentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract