Technology Sun Tracking System for Solar Power Plants Base on Recurrent Neural Networks

Haris, Abdul; Wahjuni, Sri; Sukoco, Heru; Rahmawan, Hendra; Neyman, Shelvie Nidya; Sikumbang, Hengki; Elly, Muhammad Jafar

doi:10.2991/aer.k.201221.038

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…Further, most of the papers that included data normalization used the Min-Max scaling approach, which adjusts the data value to a range between 0 and 1 or -1 and +1. The authors in [102,106,116,117,121,125,126,129,131,132] used the Min-Max scaling approach to normalize their data. On the other hand, the authors in [111,133] used the standard scaler approach to normalize the data to have a zero mean.…”

Section: Which Data Preprocessing Methods Are Applied To Solar Tracke...mentioning

confidence: 99%

“…The hidden layer comprised five nodes, whereas the final layer had two nodes. Similarly, Haris et al [131] proposed an RNN method to optimize a single-axis solar tracker with a closed-loop control mechanism. The RNN model used a single hidden layer structure and a hyperbolic tangent activation function.…”

Section: Review Of DL Modelsmentioning

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

“…The data used in this study was obtained in one day from 7 am to 9 am with a data sampling interval of 1 second. In addition, Haris et al [131] used LDR data to optimize the performance of a solar tracking system to balance the energy captured and the power stored in the solar batteries. The data used in this study accounted for four seasons, including rainy weather, bright sun, cloudy, and sunny with thin cloud cover.…”

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: Which Data Preprocessing Methods Are Applied To Solar Tracke...mentioning

confidence: 99%

Section: Review Of DL Modelsmentioning

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

“…Given the time-series-based nature of solar tracking and its tabular data representation, most researchers use RNN and LSTM models to track the sun's position as they are well suited for sequential data. For example, Haris et al [41] used tabular data to train an RNN model for optimized solar tracking. The study's results showed that the RNN model could accurately predict the sun's position.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multistage Hybrid Deep Learning Model for Enhanced Solar Tracking

Mulenga,

Phiri,

Simukonda

et al. 2023

IEEE Access

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Solar tracking helps maximize the efficiency of solar applications, such as photovoltaic (PV) solar panels. In the recent past, machine learning (ML) techniques have been extensively used to implement automatic solar tracking. However, applying predictive models in solar trackers is a non-trivial task due to the randomness and non-linearity of meteorological data, limiting their ability to clearly represent the underlying data patterns. Most existing predictive models take a monolithic approach to addressing limitations related to meteorological data, thereby limiting their performance. Therefore, this paper proposes a deep hybrid learning (DHL) model to enhance solar tracking performance. Furthermore, the proposed model improves feature representation in the data by using combined normalization methods and conversion of numerical data to images. In a nutshell, the model integrates sine and cosine transformations (SCT) to reveal cyclical patterns in the data, sigmoid and minimum-maximum data transformations to scale the data to a Gaussian distribution, and Gramian Angular Fields (GAF) to convert tabular data into 2D image representation to take advantage of the feature extraction capability of a convolutional neural network (CNN). The model also utilizes longterm short-term memory (LSTM) and gated recurrent units (GRU) for both spatial and temporal feature extraction. The results show that the aggregation of the above-mentioned methods significantly enhances solar tracking. The proposed hybrid model outperforms existing methods on a publicly available dataset, achieving outstanding performance with MAE, MAPE, and RMSE scores of 0.0073, 1.4635, and 0.0097, respectively.

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Deep learning techniques for solar tracking systems: A systematic literature review, research challenges, and open research directions

et al. 2023

View full text Add to dashboard Cite

Technology Sun Tracking System for Solar Power Plants Base on Recurrent Neural Networks

Cited by 4 publications

References 3 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 Multistage Hybrid Deep Learning Model for Enhanced Solar Tracking

Deep learning techniques for solar tracking systems: A systematic literature review, research challenges, and open research directions

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