RETRACTED: A prediction model for the performance of solar photovoltaic-thermoelectric systems utilizing various semiconductors via optimal surrogate machine learning methods

Alghamdi, Hisham; Maduabuchi, Chika; Albaker, Abdullah; Alatawi, Ibrahim; Alsenani, Theyab R.; Alsafran, Ahmed S.; Almalaq, Abdulaziz; AlAqil, Mohammed; Abdelmohimen, Mostafa A.H.; Alkhedher, Mohammad

doi:10.1016/j.jestch.2023.101363

Cited by 7 publications

(4 citation statements)

References 80 publications

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“…, the value obtained by solving Equation ( 10) and the minimum value of S k are shown in Equations ( 11) and (12).…”

Section: Methodology 211 Pso-xgboost Modelmentioning

confidence: 99%

“…The prediction models can be classified into several categories such as time series models [8], regression models [9], and machine learning models [10]. Machine learning models, represented by the artificial neural network (ANN) [11][12][13], are widely used for PV power prediction. An approach involving the optimized and diversified ANN was proposed for PV power prediction [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models

Li,

Zhu,

Tao

et al. 2023

Processes

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A photovoltaic (PV)-powered electric motor is used for hose-drawn traveler driving instead of a water turbine to achieve high transmission efficiency. PV power generation (PVPG) is affected by different meteorological conditions, resulting in different power generation of PV panels for a hose-drawn traveler. In the above situation, the hose-drawn traveler may experience deficit power generation. The reasonable determination of the PV panel capacity is crucial. Predicting the PVPG is a prerequisite for the reasonable determination of the PV panel capacity. Therefore, it is essential to develop a method for accurately predicting PVPG. Extreme gradient boosting (XGBoost) is currently an outstanding machine learning model for prediction performance, but its hyperparameters are difficult to set. Thus, the XGBoost model based on particle swarm optimization (PSO-XGBoost) is applied for PV power prediction in this study. The PSO algorithm is introduced to optimize hyperparameters in XGBoost model. The meteorological data are segmented into four seasons to develop tailored prediction models, ensuring accurate prediction of PVPG in four seasons for hose-drawn travelers. The input variables of the models include solar irradiance, time, and ambient temperature. The prediction accuracy and stability of the model is then assessed statistically. The predictive accuracy and stability of PV power prediction by the PSO-XGBoost model are higher compared to the XGBoost model. Finally, application of the PSO-XGBoost model is implemented based on meteorological data.

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“…, the value obtained by solving Equation ( 10) and the minimum value of S k are shown in Equations ( 11) and (12).…”

Section: Methodology 211 Pso-xgboost Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models

Li,

Zhu,

Tao

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…In this case, the model is a firstorder polynomial [10], hence the name "Poly-1-Order." The term "optimal" refers to the fact that the model parameters are determined through an optimization process that minimizes the difference between the actual system response and the predicted response of the model [4]. Thus, the OP-1 model is a mathematical representation used in Electrical Engineering to describe the behavior of linear systems with a single input and a single output which is derived through an optimization process that minimizes the difference between the actual system response and the predicted response of the model [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an Optimal Poly-1-Order (OP-1) Model for Approximating Solar Photovoltaic (PV) Power Generation

Ene, I. P. and Omorogiuwa, E.

2024

JCMPS

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This study was conducted to develop and evaluate the Optimal Poly-1-Order (OP-1) model for approximating solar photovoltaic (PV) power generation. Using a mixed research method, the study employed Ibrahim's simulation and prediction of grid-connected PV system theory with two objectives and their corresponding research questions. The study gathered primary and secondary data to approximate the implementation of a solar-PV system with an OP-1 model for generating electricity: optimizing energy production, load demands, and financial viability in the medical hostel facility of the University of Port Harcourt, Rivers State, Nigeria. With the use of simulation and descriptive methods of data analysis, results showed that the lighting system had 400 lights, each with 12W power. It operated for a total of 18 hours. Daily power consumption was 36,400 Wh. More so, it showed that 60 fans with 100W power were used during the same hours, resulting in a daily power usage of 108,000 Wh. Based on a comprehensive economic evaluation, the OP-1 solar-PV system was found to be economically viable for powering the medical hostel. The system met electricity demand, resulting in a remarkable 407% ROI and substantial savings for the grid, despite a lower optimized size of 193kW compared to the base peak generation of 383.90k. The study concluded and recommended that the proposed OP-1 Solar-PV power plant can meet the facility's electricity needs with a peak generation of 383.90kW and detailed energy analysis. Deploying this efficient solar-PV setup guarantees reliable and green electricity for the Medical Hostel, slashing the campus's carbon footprint and grid reliance.

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“…For example, ML can be used to predict the output of the PV/T based on factors such as weather conditions, time of day, and energy demand as shown in Figure 6D. This can allow for better control and management of the system, leading to improved efficiency and energy savings (Alghamdi et al, 2023). Additionally, ML can be used to identify patterns and trends in the data generated by the PV/T, such as energy consumption and generation, temperature, and weather conditions (Yousif and Kazem, 2021).…”

Section: Briefing Of Solar Pv/t Technology Innovationmentioning

confidence: 99%

Photovoltaic/thermal systems for carbon dioxide mitigation applications: a review

Kazem

Al‐Waeli

Chaichan

et al. 2023

Front. Built Environ.

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The urgent need to mitigate carbon dioxide (CO2) emissions and address climate change has led to increasing interest in renewable energy technologies. There are other promising energy generation systems, including photovoltaic/thermal (PV/T) systems. This paper provides a comprehensive review of PV/T systems for CO2 mitigation applications. PV/T systems are reviewed according to their principles, their design configurations, and their performance characteristics. Various types of PV/T systems, including flat-plate, concentrating, hybrid, and novel designs, are discussed, along with their advantages and limitations. In addition to examining PV/T systems as part of the integration of building systems with renewable energy sources and energy storage technologies. Furthermore, the environmental and economic aspects of PV/T systems, as well as their potential for CO2 mitigation in various applications such as residential, commercial, industrial, and agricultural sectors, are critically analyzed. Finally, future research directions and challenges in the field of PV/T systems for CO2 mitigation are outlined. The purpose of this review is to provide researchers, policymakers, and practitioners with information on how PV/T systems can be applied to reduce CO2 emissions and promote sustainable building design.

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RETRACTED: A prediction model for the performance of solar photovoltaic-thermoelectric systems utilizing various semiconductors via optimal surrogate machine learning methods

Cited by 7 publications

References 80 publications

Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models

Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models

Development of an Optimal Poly-1-Order (OP-1) Model for Approximating Solar Photovoltaic (PV) Power Generation

Photovoltaic/thermal systems for carbon dioxide mitigation applications: a review

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