A Comprehensive Review of the Application of Machine Learning in Fabrication and Implementation of Photovoltaic Systems

Datta, Srabanti; Baul, Anik; Sarker, Gobinda Chandra; Sadhu, Pintu Kumar; Hodges, Deidra

doi:10.1109/access.2023.3298542

Cited by 10 publications

(3 citation statements)

References 136 publications

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“…The XGBoost algorithm, which has been widely used in the field of predicting the properties of PSCs, was chosen for this experiment. 10,30 XGBoost extends the cost function to a second-order Taylor expansion based on the traditional gradient boosting tree and adds a regularization term to penalize model complexity. As a result, XGBoost enhances its generalization ability while learning more details (more details about XGBoost can be found in Note S1.2).…”

Section: Resultsmentioning

confidence: 99%

“…By introducing random factors into the model and training data with different training-test splits and repeating the above steps 500 times, the accuracy of the model before and after feature screening and the fluctuations caused by random factors are compared, as depicted in Figure S4. The XGBoost algorithm, which has been widely used in the field of predicting the properties of PSCs, was chosen for this experiment. , XGBoost extends the cost function to a second-order Taylor expansion based on the traditional gradient boosting tree and adds a regularization term to penalize model complexity. As a result, XGBoost enhances its generalization ability while learning more details (more details about XGBoost can be found in Note S1.2).…”

Section: Resultsmentioning

confidence: 99%

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Temporal Decoupling-Based Machine Learning Framework for Precise Efficiency Prediction in Perovskite Solar Cells

Yang,

Meng

et al. 2024

J. Phys. Chem. C

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The rapid and accurate identification of potential high-efficiency design strategies for perovskite solar cells (PSCs) is of paramount importance in advancing their development and commercialization. However, the application of machine learning (ML) algorithms in this field is hindered by unstable PSC data sets (e.g., time-related noise and data imbalance). Here, we introduce a ML framework specifically tailored for temporal decoupling through feature engineering and uncertainty modeling (noise processing techniques) to accurately predict the efficiency of PSCs. In our framework, we utilize one-hot encoding and feature fusion methods to extract features from a shared data set encompassing perovskite material, processing, and PSC architecture. After temporal decoupling, our ML model shows an outstanding precision of 96.88% and a specificity of 99.3% for high-efficiency devices and is successfully applied to predict PSC efficiencies in the period between 2021 and 2023. This temporal decoupling ML framework also reveals hidden relationships between features and efficiency through cross-feature analysis. Our work demonstrates the potential of ML for predicting performance and elucidates the associated mechanisms, accelerating PSC commercialization and reducing costs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Temporal Decoupling-Based Machine Learning Framework for Precise Efficiency Prediction in Perovskite Solar Cells

Yang,

Meng

et al. 2024

J. Phys. Chem. C

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“…The presented methodology improves the nonlinear data representation of solar behavior. The authors in [25] evaluated more than 100 research articles to investigate ML implementation in solar cell fabrication. The findings show that the Random Forest (RF), linear regression (LR), XGBoost, and artificial neural network (ANN) algorithms are the most commonly employed techniques.…”

mentioning

confidence: 99%

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

Dobrilovic,

Pekez,

Ognjenovic

et al. 2024

Applied Sciences

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The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.

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Nature-inspired enhancement in power conversion efficiency of bio-photovoltaics using photosynthetic protein complexes

Khanmohammadi Chenab,

Zamani Meymian,

Bagheri

et al. 2025

Materials Science in Semiconductor Processing

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A Comprehensive Review of the Application of Machine Learning in Fabrication and Implementation of Photovoltaic Systems

Cited by 10 publications

References 136 publications

Temporal Decoupling-Based Machine Learning Framework for Precise Efficiency Prediction in Perovskite Solar Cells

Temporal Decoupling-Based Machine Learning Framework for Precise Efficiency Prediction in Perovskite Solar Cells

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

Nature-inspired enhancement in power conversion efficiency of bio-photovoltaics using photosynthetic protein complexes

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