FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4377018524> ?p ?o ?g. }

• W4377018524 endingPage "117186" @default.
• W4377018524 startingPage "117186" @default.
• W4377018524 abstract "This paper proposes artificial neural network (ANN) and regression models for photovoltaic modules power output predictions and investigates the effects of climatic conditions and operating temperature on the estimated output. The models use six days of experimental data creating a large dataset of 172,800 × 7. After data preprocessing, the appropriate attributes were selected as inputs and taken into account as features; solar irradiation, ambient air and module temperature, wind speed, and relative humidity, while the power generation as a target. In light of these data, the effect of training algorithm on the predictive performance of the ANN model was investigated. Results show that solar irradiation, ambient and module temperatures are key factors in predicting PV module power generation, as these variables are strongly correlated with PV power output. Moreover, the Levenberg-Marquardt algorithm was found to be the best training procedure. The ANN model demonstrated higher accuracy than the developed multiple linear regression models. However, the proposed Rational-Power-Law (RPL) and Power-Law (PL) models were able to capture the nonlinearity in the system, as assessed by coefficient of determination (R2) and the Mean Absolute Error (MAE), and successfully supplied a very high level of precision. The ANN, and both RPL and PL models provided comparable performance, attaining an R2 of 0.997, 0.998 and 0.996, and a MAE of 1.998, 1.156, and 1.242, respectively, when compared to experimental results. Furthermore, models proposed in this study were evaluated and compared with others available in literature and have demonstrated superior performance and better accuracy." @default.
• W4377018524 created "2023-05-19" @default.
• W4377018524 creator A5016412474 @default.
• W4377018524 creator A5047420474 @default.
• W4377018524 creator A5048998369 @default.
• W4377018524 creator A5063583217 @default.
• W4377018524 creator A5068875533 @default.
• W4377018524 creator A5079193434 @default.
• W4377018524 creator A5091972025 @default.
• W4377018524 date "2023-07-01" @default.
• W4377018524 modified "2023-10-16" @default.
• W4377018524 title "Solar photovoltaic power prediction using artificial neural network and multiple regression considering ambient and operating conditions" @default.
• W4377018524 cites W1972612676 @default.
• W4377018524 cites W1989649856 @default.
• W4377018524 cites W1990875890 @default.
• W4377018524 cites W2001993733 @default.
• W4377018524 cites W2003171375 @default.
• W4377018524 cites W2021540113 @default.
• W4377018524 cites W2028782916 @default.
• W4377018524 cites W2029149883 @default.
• W4377018524 cites W2193726160 @default.
• W4377018524 cites W2234427394 @default.
• W4377018524 cites W2275327100 @default.
• W4377018524 cites W2506026375 @default.
• W4377018524 cites W2610219179 @default.
• W4377018524 cites W2752250962 @default.
• W4377018524 cites W2768066635 @default.
• W4377018524 cites W2776691905 @default.
• W4377018524 cites W2781167107 @default.
• W4377018524 cites W2790381093 @default.
• W4377018524 cites W2791958283 @default.
• W4377018524 cites W2792131305 @default.
• W4377018524 cites W2801596954 @default.
• W4377018524 cites W2834494841 @default.
• W4377018524 cites W2886368164 @default.
• W4377018524 cites W2903061848 @default.
• W4377018524 cites W2903662170 @default.
• W4377018524 cites W2903831041 @default.
• W4377018524 cites W2904726219 @default.
• W4377018524 cites W2942485381 @default.
• W4377018524 cites W2984252744 @default.
• W4377018524 cites W3003713939 @default.
• W4377018524 cites W3011512429 @default.
• W4377018524 cites W3108620569 @default.
• W4377018524 cites W3111374748 @default.
• W4377018524 cites W3128276472 @default.
• W4377018524 cites W3129734871 @default.
• W4377018524 cites W3146534011 @default.
• W4377018524 cites W3204229200 @default.
• W4377018524 cites W3214910795 @default.
• W4377018524 cites W3215258715 @default.
• W4377018524 cites W4200238335 @default.
• W4377018524 cites W4200550314 @default.
• W4377018524 cites W4282013003 @default.
• W4377018524 cites W4300840064 @default.
• W4377018524 cites W4309780187 @default.
• W4377018524 cites W4311907906 @default.
• W4377018524 cites W4312577556 @default.
• W4377018524 cites W4320035669 @default.
• W4377018524 cites W858921981 @default.
• W4377018524 doi "https://doi.org/10.1016/j.enconman.2023.117186" @default.
• W4377018524 hasPublicationYear "2023" @default.
• W4377018524 type Work @default.
• W4377018524 citedByCount "1" @default.
• W4377018524 countsByYear W43770185242023 @default.
• W4377018524 crossrefType "journal-article" @default.
• W4377018524 hasAuthorship W4377018524A5016412474 @default.
• W4377018524 hasAuthorship W4377018524A5047420474 @default.
• W4377018524 hasAuthorship W4377018524A5048998369 @default.
• W4377018524 hasAuthorship W4377018524A5063583217 @default.
• W4377018524 hasAuthorship W4377018524A5068875533 @default.
• W4377018524 hasAuthorship W4377018524A5079193434 @default.
• W4377018524 hasAuthorship W4377018524A5091972025 @default.
• W4377018524 hasConcept C105795698 @default.
• W4377018524 hasConcept C11413529 @default.
• W4377018524 hasConcept C119599485 @default.
• W4377018524 hasConcept C119857082 @default.
• W4377018524 hasConcept C121332964 @default.
• W4377018524 hasConcept C122383733 @default.
• W4377018524 hasConcept C127413603 @default.
• W4377018524 hasConcept C139945424 @default.
• W4377018524 hasConcept C152877465 @default.
• W4377018524 hasConcept C153294291 @default.
• W4377018524 hasConcept C161067210 @default.
• W4377018524 hasConcept C163258240 @default.
• W4377018524 hasConcept C33923547 @default.
• W4377018524 hasConcept C41008148 @default.
• W4377018524 hasConcept C41291067 @default.
• W4377018524 hasConcept C46889948 @default.
• W4377018524 hasConcept C48921125 @default.
• W4377018524 hasConcept C50644808 @default.
• W4377018524 hasConcept C62520636 @default.
• W4377018524 hasConcept C83546350 @default.
• W4377018524 hasConceptScore W4377018524C105795698 @default.
• W4377018524 hasConceptScore W4377018524C11413529 @default.
• W4377018524 hasConceptScore W4377018524C119599485 @default.
• W4377018524 hasConceptScore W4377018524C119857082 @default.
• W4377018524 hasConceptScore W4377018524C121332964 @default.

• next