FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376604807> ?p ?o ?g. }

• W4376604807 endingPage "127824" @default.
• W4376604807 startingPage "127824" @default.
• W4376604807 abstract "The generation forecast helps to develop the scheduling plan of power sources, build a power balance system and ensure the adequacy of power supply to the power system at the macro level. Therefore, a novel structurally adaptive discrete grey Bernoulli model, called DHGBM(1,1), is proposed in this paper for the prediction of power generation. Firstly, the Hausdorff fractional order cumulative generation operation makes it possible to achieve new information prioritisation. As for the structure of the model, we have extended the nonlinear grey Bernoulli model with the introduction of a nonlinear dynamic structure term, which allows the model to simulate variable generation data and improves the adaptability of the model. In a comparative test of the algorithms, the well-performing differential evolution algorithm was used for hyperparameter optimisation of the model to obtain better predictive performance. Monte Carlo simulations and probability density analysis verified the excellent robustness of the model. In addition, through the simulation tests, we obtained a reasonable range of parameters for the structural terms, thus limiting the complexity of the model, and the phenomenon of model overfitting was effectively prevented. Finally, five actual cases of China's hydro, China's thermal, China's nuclear, China's wind power generation and China's electricity exports are predicted." @default.
• W4376604807 created "2023-05-17" @default.
• W4376604807 creator A5012278873 @default.
• W4376604807 creator A5032613807 @default.
• W4376604807 creator A5041321290 @default.
• W4376604807 creator A5059819810 @default.
• W4376604807 creator A5078036018 @default.
• W4376604807 creator A5081736389 @default.
• W4376604807 date "2023-09-01" @default.
• W4376604807 modified "2023-09-28" @default.
• W4376604807 title "Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model" @default.
• W4376604807 cites W1992665910 @default.
• W4376604807 cites W2061438946 @default.
• W4376604807 cites W2083038452 @default.
• W4376604807 cites W2152195021 @default.
• W4376604807 cites W2799398830 @default.
• W4376604807 cites W2804202111 @default.
• W4376604807 cites W2914451237 @default.
• W4376604807 cites W2922456124 @default.
• W4376604807 cites W2982236294 @default.
• W4376604807 cites W2996549886 @default.
• W4376604807 cites W3017210658 @default.
• W4376604807 cites W3034081852 @default.
• W4376604807 cites W3042311080 @default.
• W4376604807 cites W3047173614 @default.
• W4376604807 cites W3049703542 @default.
• W4376604807 cites W3089052061 @default.
• W4376604807 cites W3120779329 @default.
• W4376604807 cites W3133901049 @default.
• W4376604807 cites W3153062128 @default.
• W4376604807 cites W3162105028 @default.
• W4376604807 cites W3181384335 @default.
• W4376604807 cites W3187411832 @default.
• W4376604807 cites W3214208331 @default.
• W4376604807 cites W4200000569 @default.
• W4376604807 cites W4211240201 @default.
• W4376604807 cites W4221056556 @default.
• W4376604807 cites W4225278767 @default.
• W4376604807 cites W4283652767 @default.
• W4376604807 cites W4284991160 @default.
• W4376604807 cites W4290694919 @default.
• W4376604807 cites W4291109442 @default.
• W4376604807 cites W4293107966 @default.
• W4376604807 cites W4293224947 @default.
• W4376604807 cites W4293225020 @default.
• W4376604807 cites W4307405511 @default.
• W4376604807 cites W4313215550 @default.
• W4376604807 cites W4319321011 @default.
• W4376604807 doi "https://doi.org/10.1016/j.energy.2023.127824" @default.
• W4376604807 hasPublicationYear "2023" @default.
• W4376604807 type Work @default.
• W4376604807 citedByCount "0" @default.
• W4376604807 crossrefType "journal-article" @default.
• W4376604807 hasAuthorship W4376604807A5012278873 @default.
• W4376604807 hasAuthorship W4376604807A5032613807 @default.
• W4376604807 hasAuthorship W4376604807A5041321290 @default.
• W4376604807 hasAuthorship W4376604807A5059819810 @default.
• W4376604807 hasAuthorship W4376604807A5078036018 @default.
• W4376604807 hasAuthorship W4376604807A5081736389 @default.
• W4376604807 hasConcept C104317684 @default.
• W4376604807 hasConcept C119599485 @default.
• W4376604807 hasConcept C121332964 @default.
• W4376604807 hasConcept C126255220 @default.
• W4376604807 hasConcept C127413603 @default.
• W4376604807 hasConcept C154945302 @default.
• W4376604807 hasConcept C158622935 @default.
• W4376604807 hasConcept C162324750 @default.
• W4376604807 hasConcept C163258240 @default.
• W4376604807 hasConcept C177606310 @default.
• W4376604807 hasConcept C185592680 @default.
• W4376604807 hasConcept C187736073 @default.
• W4376604807 hasConcept C22019652 @default.
• W4376604807 hasConcept C33923547 @default.
• W4376604807 hasConcept C41008148 @default.
• W4376604807 hasConcept C423512 @default.
• W4376604807 hasConcept C50644808 @default.
• W4376604807 hasConcept C55493867 @default.
• W4376604807 hasConcept C62520636 @default.
• W4376604807 hasConcept C63479239 @default.
• W4376604807 hasConcept C78600449 @default.
• W4376604807 hasConceptScore W4376604807C104317684 @default.
• W4376604807 hasConceptScore W4376604807C119599485 @default.
• W4376604807 hasConceptScore W4376604807C121332964 @default.
• W4376604807 hasConceptScore W4376604807C126255220 @default.
• W4376604807 hasConceptScore W4376604807C127413603 @default.
• W4376604807 hasConceptScore W4376604807C154945302 @default.
• W4376604807 hasConceptScore W4376604807C158622935 @default.
• W4376604807 hasConceptScore W4376604807C162324750 @default.
• W4376604807 hasConceptScore W4376604807C163258240 @default.
• W4376604807 hasConceptScore W4376604807C177606310 @default.
• W4376604807 hasConceptScore W4376604807C185592680 @default.
• W4376604807 hasConceptScore W4376604807C187736073 @default.
• W4376604807 hasConceptScore W4376604807C22019652 @default.
• W4376604807 hasConceptScore W4376604807C33923547 @default.
• W4376604807 hasConceptScore W4376604807C41008148 @default.
• W4376604807 hasConceptScore W4376604807C423512 @default.
• W4376604807 hasConceptScore W4376604807C50644808 @default.
• W4376604807 hasConceptScore W4376604807C55493867 @default.

• next