FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328112737> ?p ?o ?g. }

• W4328112737 endingPage "101363" @default.
• W4328112737 startingPage "101363" @default.
• W4328112737 abstract "This research focuses on finding the best surrogate performance prediction model for a solar photovoltaic-thermoelectric (PV-TE) module with different semiconductor materials. The need for a surrogate machine learning model arises due to the inefficiency of the current numerical simulations used to assess the performance of the device. The study introduces several surrogate machine learning models, such as recurrent, time delay, and regular artificial neural networks (ANNs), that are trained using expensive finite element generated data when the operating parameters of the system are altered. These parameters include the optical concentration ratio, cooling coefficient, wind speed, air temperature, glass emissivity, semiconductor dimensions, external load resistance, and thermoelectric current. Despite the time-intensive and costly data generation method, 714 datapoints were produced and utilized to train the surrogate machine learning models for improved performance prediction and optimization. The results indicate that the optimal machine learning model for solar PV-TE performance modelling was the ANN architecture with two hidden layers and five neurons per layer. Furthermore, lithium nitride oxide PV-TE showed a 65% improvement over the popular bismuth telluride PV-TE when tested under 25 Suns. The surrogate ANN also outperformed the conventional numerical simulations by 10,000 times using the same computing resources. Finally, the study suggests the potential of recurrent and time delay neural networks for modelling time series PV-TE data in the future." @default.
• W4328112737 created "2023-03-22" @default.
• W4328112737 creator A5007037967 @default.
• W4328112737 creator A5024419134 @default.
• W4328112737 creator A5024658515 @default.
• W4328112737 creator A5026262845 @default.
• W4328112737 creator A5032077431 @default.
• W4328112737 creator A5032618055 @default.
• W4328112737 creator A5042520531 @default.
• W4328112737 creator A5049227570 @default.
• W4328112737 creator A5053367614 @default.
• W4328112737 creator A5073539822 @default.
• W4328112737 date "2023-04-01" @default.
• W4328112737 modified "2023-10-14" @default.
• W4328112737 title "A prediction model for the performance of solar photovoltaic-thermoelectric systems utilizing various semiconductors via optimal surrogate machine learning methods" @default.
• W4328112737 cites W1942247201 @default.
• W4328112737 cites W1966154590 @default.
• W4328112737 cites W1967429206 @default.
• W4328112737 cites W1973912533 @default.
• W4328112737 cites W1979116483 @default.
• W4328112737 cites W1980184297 @default.
• W4328112737 cites W2005077059 @default.
• W4328112737 cites W2017540740 @default.
• W4328112737 cites W2028341037 @default.
• W4328112737 cites W2030963284 @default.
• W4328112737 cites W2056675229 @default.
• W4328112737 cites W2059019633 @default.
• W4328112737 cites W2061043032 @default.
• W4328112737 cites W2065499314 @default.
• W4328112737 cites W2076514882 @default.
• W4328112737 cites W2079342035 @default.
• W4328112737 cites W2082971053 @default.
• W4328112737 cites W2087410972 @default.
• W4328112737 cites W2097139389 @default.
• W4328112737 cites W2109530046 @default.
• W4328112737 cites W2118672835 @default.
• W4328112737 cites W2121232771 @default.
• W4328112737 cites W2140207729 @default.
• W4328112737 cites W2151592156 @default.
• W4328112737 cites W2171800554 @default.
• W4328112737 cites W2175341618 @default.
• W4328112737 cites W2208702937 @default.
• W4328112737 cites W2272377834 @default.
• W4328112737 cites W2308452528 @default.
• W4328112737 cites W2402146185 @default.
• W4328112737 cites W2403020391 @default.
• W4328112737 cites W2415829859 @default.
• W4328112737 cites W2510261629 @default.
• W4328112737 cites W2546947825 @default.
• W4328112737 cites W2561740280 @default.
• W4328112737 cites W2754031483 @default.
• W4328112737 cites W2760948241 @default.
• W4328112737 cites W2766275656 @default.
• W4328112737 cites W2775631786 @default.
• W4328112737 cites W2790206624 @default.
• W4328112737 cites W2790941383 @default.
• W4328112737 cites W2805090114 @default.
• W4328112737 cites W2806149722 @default.
• W4328112737 cites W2806940859 @default.
• W4328112737 cites W2897279290 @default.
• W4328112737 cites W2908802621 @default.
• W4328112737 cites W2944120742 @default.
• W4328112737 cites W2944675074 @default.
• W4328112737 cites W2951781175 @default.
• W4328112737 cites W2972286303 @default.
• W4328112737 cites W2996728536 @default.
• W4328112737 cites W3005206225 @default.
• W4328112737 cites W3006210364 @default.
• W4328112737 cites W3009439247 @default.
• W4328112737 cites W3023231604 @default.
• W4328112737 cites W3035078871 @default.
• W4328112737 cites W3037358073 @default.
• W4328112737 cites W3047541731 @default.
• W4328112737 cites W3056377790 @default.
• W4328112737 cites W3081799531 @default.
• W4328112737 cites W3082366755 @default.
• W4328112737 cites W3115891715 @default.
• W4328112737 cites W3116754294 @default.
• W4328112737 cites W3127926047 @default.
• W4328112737 cites W3128706718 @default.
• W4328112737 cites W3144959632 @default.
• W4328112737 cites W3163660633 @default.
• W4328112737 cites W3175098232 @default.
• W4328112737 cites W3196781046 @default.
• W4328112737 cites W3200399333 @default.
• W4328112737 cites W3204586475 @default.
• W4328112737 cites W3211842400 @default.
• W4328112737 cites W3212327177 @default.
• W4328112737 cites W3213670609 @default.
• W4328112737 cites W3214286852 @default.
• W4328112737 cites W4200551466 @default.
• W4328112737 cites W4283574155 @default.
• W4328112737 cites W4319319751 @default.
• W4328112737 cites W4319959420 @default.
• W4328112737 doi "https://doi.org/10.1016/j.jestch.2023.101363" @default.
• W4328112737 hasPublicationYear "2023" @default.
• W4328112737 type Work @default.
• W4328112737 citedByCount "2" @default.

• next