FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2901004617> ?p ?o ?g. }

• W2901004617 endingPage "212" @default.
• W2901004617 startingPage "186" @default.
• W2901004617 abstract "Abstract Accurate estimation of global solar radiation (Rs) is essential to the design and assessment of solar energy utilization systems. Existing empirical and machine learning models for estimating Rs from sunshine duration were comprehensively reviewed. The performances of 12 empirical model forms and 12 machine learning algorithms for estimating daily Rs were further evaluated in different climatic zones of China as a case study, i.e. the temperate continental zone (TCZ), temperate monsoon zone (TMZ), mountain plateau zone (MPZ) and (sub)tropical monsoon zone (SMZ). The best-performing model at each station and the overall best model for each climatic zone were selected based on six statistical indictors, a global performance index (GPI) and computational costs (computational time and memory usage). The results revealed that the machine learning models (RMSE: 2.055–2.751 MJ m−2 d−1; NRMSE: 12.8–21.3%; R2: 0.839–0.936) generally outperformed the empirical models (RMSE: 2.118–3.540 MJ m−2 d−1; NRMSE: 12.1–27.5%; R2: 0.834–0.935) in terms of prediction accuracy. The cubic model (M3), modified linear-logarithmic model (M5) and power model (M10) attained generally better ranks among empirical models based on GPI. M3 was the top-ranked model in TMZ and MPZ, while general best performance was obtained by M5 and M2 in SMZ and TCZ, respectively. ANFIS, ELM, LSSVM and MARS obtained generally better performance among machine learning models, with the overall best ranking by ANFIS in TCZ and SMZ and by ELM in MPZ and SMZ. XGBoost (8.1 s and 74.2 MB), M5Tree (11.3 s and 29.7 MB), GRNN (12.3 s and 295.3 MB), MARS (14.4 s and 42.6 MB), MLP (22.4 s and 41.3 MB) and ANFIS (29.8 s and 23.1 MB) showed relatively small computational time and memory usage. Comprehensively considering both the prediction accuracy and computational costs, ANFIS is highly recommended, while MARS and XGBoost are also promising models for daily Rs estimation." @default.
• W2901004617 created "2018-11-29" @default.
• W2901004617 creator A5001348758 @default.
• W2901004617 creator A5008839835 @default.
• W2901004617 creator A5061220632 @default.
• W2901004617 creator A5061221648 @default.
• W2901004617 creator A5062668689 @default.
• W2901004617 creator A5063213100 @default.
• W2901004617 creator A5077816517 @default.
• W2901004617 date "2019-02-01" @default.
• W2901004617 modified "2023-10-17" @default.
• W2901004617 title "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China" @default.
• W2901004617 cites W1124861640 @default.
• W2901004617 cites W1191826283 @default.
• W2901004617 cites W1452482314 @default.
• W2901004617 cites W1469381315 @default.
• W2901004617 cites W1847289356 @default.
• W2901004617 cites W1895707564 @default.
• W2901004617 cites W1913657127 @default.
• W2901004617 cites W1967457280 @default.
• W2901004617 cites W1967873610 @default.
• W2901004617 cites W1968365296 @default.
• W2901004617 cites W1972134622 @default.
• W2901004617 cites W1973322472 @default.
• W2901004617 cites W1973600008 @default.
• W2901004617 cites W1975778276 @default.
• W2901004617 cites W1976049118 @default.
• W2901004617 cites W1976523109 @default.
• W2901004617 cites W1988783242 @default.
• W2901004617 cites W1990389564 @default.
• W2901004617 cites W1995384696 @default.
• W2901004617 cites W2002404570 @default.
• W2901004617 cites W2004049475 @default.
• W2901004617 cites W2007375474 @default.
• W2901004617 cites W2011129798 @default.
• W2901004617 cites W2011878321 @default.
• W2901004617 cites W2012214460 @default.
• W2901004617 cites W2017440032 @default.
• W2901004617 cites W2017732481 @default.
• W2901004617 cites W2019207321 @default.
• W2901004617 cites W2026324524 @default.
• W2901004617 cites W2031347135 @default.
• W2901004617 cites W2031812108 @default.
• W2901004617 cites W2035542086 @default.
• W2901004617 cites W2036908107 @default.
• W2901004617 cites W2037940492 @default.
• W2901004617 cites W2039395979 @default.
• W2901004617 cites W2040735756 @default.
• W2901004617 cites W2043590142 @default.
• W2901004617 cites W2044451649 @default.
• W2901004617 cites W2050985317 @default.
• W2901004617 cites W2052437577 @default.
• W2901004617 cites W2054052655 @default.
• W2901004617 cites W2055476972 @default.
• W2901004617 cites W2056614595 @default.
• W2901004617 cites W2056657701 @default.
• W2901004617 cites W2057831526 @default.
• W2901004617 cites W2059749951 @default.
• W2901004617 cites W2061505690 @default.
• W2901004617 cites W2061632729 @default.
• W2901004617 cites W2064849162 @default.
• W2901004617 cites W2065532584 @default.
• W2901004617 cites W2070493638 @default.
• W2901004617 cites W2072904017 @default.
• W2901004617 cites W2074214528 @default.
• W2901004617 cites W2074298417 @default.
• W2901004617 cites W2075052532 @default.
• W2901004617 cites W2075891641 @default.
• W2901004617 cites W2076256832 @default.
• W2901004617 cites W2078953819 @default.
• W2901004617 cites W2081163158 @default.
• W2901004617 cites W2081380866 @default.
• W2901004617 cites W2084503059 @default.
• W2901004617 cites W2086350890 @default.
• W2901004617 cites W2088638940 @default.
• W2901004617 cites W2088782270 @default.
• W2901004617 cites W2099445341 @default.
• W2901004617 cites W2102201073 @default.
• W2901004617 cites W2106721205 @default.
• W2901004617 cites W2108325538 @default.
• W2901004617 cites W2111072639 @default.
• W2901004617 cites W2111959411 @default.
• W2901004617 cites W2116931356 @default.
• W2901004617 cites W2117699847 @default.
• W2901004617 cites W2122062543 @default.
• W2901004617 cites W2128171055 @default.
• W2901004617 cites W2128349896 @default.
• W2901004617 cites W2134412801 @default.
• W2901004617 cites W2134829952 @default.
• W2901004617 cites W2147639597 @default.
• W2901004617 cites W2216444323 @default.
• W2901004617 cites W2283737367 @default.
• W2901004617 cites W2318515285 @default.
• W2901004617 cites W2338227759 @default.
• W2901004617 cites W2338443192 @default.
• W2901004617 cites W2398936495 @default.
• W2901004617 cites W2502075365 @default.
• W2901004617 cites W2527066115 @default.

• next