Prediction of the effect of temperature on electric power in photovoltaic thermal systems based on natural zeolite plates

Metlek, Sedat; Kandilli, Canan; Kayaalp, Kıyas

doi:10.1002/er.7575

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Meltek et al proposed a model to predict the effect of the panel electric power of a photovoltaic thermal (PV-T) system using LSTM and MLF. Mean absolute error (MAE), RMSE, MAPE, and R 2 correlation coefficients were used as performance metrics [26].…”

Section: Photovoltaic Performance Analysis Approachesmentioning

confidence: 99%

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Ρουμπακιάς

Stamatelos

2023

Sustainability

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The increasing installed volume of grid-connected PV systems in modern electricity networks induces variability and uncertainty factors which must be addressed from several different viewpoints, including systems’ protection and management. This study aims to estimate the actual performance and degradation of photovoltaic (PV) parks in Central Greece after several years of operation. Monitoring data over several years are analyzed and filtered, the performance ratio and normalized efficiency are computed, and five different ANNs are employed: (i) a feed-forward network (one hidden layer); (ii) a deep feed-forward network (two hidden layers); (iii) a recurrent neural network; (iv) a cascade-forward network; and (v) a nonlinear autoregressive network. The following inputs are employed: in-plane irradiance; backsheet panel temperature; airmass; clearness index; and DC voltage of the inverter. Monitoring data from an 8-year operation of a grid-connected PV system are employed for training, testing, and validation of these networks. They act as a baseline, built from the first year, and the computed metrics act as indicators of faults or degradation. Best accuracy is reached with the DFFNN. The ANNs are trained with data from the first year of operation, and output prediction is carried out for the remaining years. Annual electricity generation exceeds 1600 kWh /kWp, and MAPE values show an increasing trend over the years. This fact indicates a possible change in PV performance.

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Section: Photovoltaic Performance Analysis Approachesmentioning

confidence: 99%

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Ρουμπακιάς

Stamatelos

2023

Sustainability

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“…Bian investigated the physicochemical quality of bruised apple using dielectric properties (Bian et al., 2020). The research results showed that the dielectric characteristic of the fruit changes with moisture content increment, density, and internal quality (Metlek et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Determination of moisture content and contaminated blank dried figs (Ficus carica L.) using dielectric property and artificial neural network

Shojaeyan,

Banakar,

Hashjin

et al. 2023

Agrosystems Geosci & Env

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Dried figs are a garden produce that must be graded after harvesting. Moisture levels and contaminated blank are two of the most critical effective elements on the marketability of dried figs, and they are highly related to fig quality. In the present research, an intelligent system was employed to classify dried figs based on moisture content levels and infected blank fruits. Capacitance characteristics, average diameter, and fruit area were all taken into account in this study. The dried fig dielectric constant was measured at six different frequency levels: 12, 22, 32, 42, 52, and 62 MHz. The best frequency was then chosen using the improved distance evaluation feature selection approach. Image processing was also used to determine the average diameter and area of the figures. Following that, the dielectric constant of the most effective frequency, the average diameter, and the area of the fruit were used as input parameters in the artificial neural network classification model to classify and describe the moisture and porosity level of the dried fig. The most essential dielectric constant information relating to moisture and porosity level was at frequencies of 22 and 52 MHz, respectively. Finally, classification accuracy of 95.7% for moisture and 91.3% for porosity level was attained. The results demonstrated the excellent performance and capabilities of the proposed approach for rating the internal quality of dried figs.

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Comparison of PV Power Production Estimation Methods Under Non-homogeneous Temperature Distribution for CPVT Systems

Demircan

Vicidomini

Calise

et al. 2023

Lecture Notes in Electrical Engineering

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Prediction of the effect of temperature on electric power in photovoltaic thermal systems based on natural zeolite plates

Cited by 5 publications

References 29 publications

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Determination of moisture content and contaminated blank dried figs (Ficus carica L.) using dielectric property and artificial neural network

Comparison of PV Power Production Estimation Methods Under Non-homogeneous Temperature Distribution for CPVT Systems

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