FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2800571504> ?p ?o ?g. }

• W2800571504 endingPage "33" @default.
• W2800571504 startingPage "13" @default.
• W2800571504 abstract "Abstract Short term load forecasting (STLF) is an important issue for an electricity power system, to enhance its management efficiency and reduce its operational costs. However, STLF is affected by lots of exogenous factors, it demonstrates complicate characteristics, particularly, the multi-dimensional nonlinearity. Therefore, it is desired to extract some valuable features embedded in the time series, to demonstrate the relationships of the nonlinearity, eventually, to improve the forecasting accuracy. Due to the superiorities of phase space reconstruction (PSR) algorithm in reconstructing the phase space of time series, and of bi-square kernel (BSK) regression model in simultaneously considering original spectral signature and spatial information, this paper proposes a novel electricity load forecasting model by hybridizing PSR algorithm with BSK regression model, namely PSR-BSK model. The electricity load data can be sufficiently reconstructed by PSR algorithm to extract the evolutionary trends of the electricity power system and the embedded valuable features information to improve the reliability of the forecasting performances. The BSK model reasonably illustrates the spatial structures among regression points and their neighbor points to receive the rules of rotation rules and disturbance in each dimension. Eventually, the proposed PSR-BSK model including multi-dimensional regression is successfully established. The short term load data from the New South Wales (NSW, Australia) market and the New York Independent System Operator (NYISO, USA) are employed to illustrate the forecasting performances with different alternative forecasting models. The results demonstrate that, in these two employed numerical examples, the proposed PSR-BSK models all significantly receive the smallest forecasting errors in terms of MAPE (less than 2.20%), RMSE (less than 30.0), and MAE (less than 2.30), and the shortest running time (less than 400 s) than other forecasting models." @default.
• W2800571504 created "2018-05-17" @default.
• W2800571504 creator A5001888021 @default.
• W2800571504 creator A5074624943 @default.
• W2800571504 creator A5075592601 @default.
• W2800571504 date "2018-08-01" @default.
• W2800571504 modified "2023-10-17" @default.
• W2800571504 title "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model" @default.
• W2800571504 cites W1711412747 @default.
• W2800571504 cites W1793209788 @default.
• W2800571504 cites W1793927692 @default.
• W2800571504 cites W1966808540 @default.
• W2800571504 cites W1966827292 @default.
• W2800571504 cites W1971714091 @default.
• W2800571504 cites W1973075395 @default.
• W2800571504 cites W1973710415 @default.
• W2800571504 cites W1975455363 @default.
• W2800571504 cites W1979373126 @default.
• W2800571504 cites W1979473671 @default.
• W2800571504 cites W1982904001 @default.
• W2800571504 cites W1983664186 @default.
• W2800571504 cites W1987938889 @default.
• W2800571504 cites W1990193139 @default.
• W2800571504 cites W1992819771 @default.
• W2800571504 cites W2001165499 @default.
• W2800571504 cites W2001863701 @default.
• W2800571504 cites W2008406084 @default.
• W2800571504 cites W2015799639 @default.
• W2800571504 cites W2042678655 @default.
• W2800571504 cites W2042792535 @default.
• W2800571504 cites W2044789214 @default.
• W2800571504 cites W2047159855 @default.
• W2800571504 cites W2060765057 @default.
• W2800571504 cites W2066089156 @default.
• W2800571504 cites W2067847508 @default.
• W2800571504 cites W2070101089 @default.
• W2800571504 cites W2070166848 @default.
• W2800571504 cites W2084675406 @default.
• W2800571504 cites W2092172498 @default.
• W2800571504 cites W2096166399 @default.
• W2800571504 cites W2133752269 @default.
• W2800571504 cites W2134383471 @default.
• W2800571504 cites W2146588145 @default.
• W2800571504 cites W2189919008 @default.
• W2800571504 cites W2288766215 @default.
• W2800571504 cites W2340247464 @default.
• W2800571504 cites W2343877001 @default.
• W2800571504 cites W2345122339 @default.
• W2800571504 cites W2521449822 @default.
• W2800571504 cites W2551161908 @default.
• W2800571504 cites W2554462191 @default.
• W2800571504 cites W2555609160 @default.
• W2800571504 cites W2559688945 @default.
• W2800571504 cites W2571945220 @default.
• W2800571504 cites W2604007484 @default.
• W2800571504 cites W2604099671 @default.
• W2800571504 cites W2620422220 @default.
• W2800571504 cites W2620511256 @default.
• W2800571504 cites W2731004805 @default.
• W2800571504 cites W2731024278 @default.
• W2800571504 cites W2747580724 @default.
• W2800571504 cites W2748523110 @default.
• W2800571504 cites W2753290709 @default.
• W2800571504 cites W2755841604 @default.
• W2800571504 cites W2766748528 @default.
• W2800571504 cites W2767440337 @default.
• W2800571504 cites W2773931999 @default.
• W2800571504 cites W2775155156 @default.
• W2800571504 cites W2779184012 @default.
• W2800571504 cites W2783813071 @default.
• W2800571504 cites W2791787990 @default.
• W2800571504 cites W2792961021 @default.
• W2800571504 doi "https://doi.org/10.1016/j.apenergy.2018.04.075" @default.
• W2800571504 hasPublicationYear "2018" @default.
• W2800571504 type Work @default.
• W2800571504 sameAs 2800571504 @default.
• W2800571504 citedByCount "146" @default.
• W2800571504 countsByYear W28005715042018 @default.
• W2800571504 countsByYear W28005715042019 @default.
• W2800571504 countsByYear W28005715042020 @default.
• W2800571504 countsByYear W28005715042021 @default.
• W2800571504 countsByYear W28005715042022 @default.
• W2800571504 countsByYear W28005715042023 @default.
• W2800571504 crossrefType "journal-article" @default.
• W2800571504 hasAuthorship W2800571504A5001888021 @default.
• W2800571504 hasAuthorship W2800571504A5074624943 @default.
• W2800571504 hasAuthorship W2800571504A5075592601 @default.
• W2800571504 hasConcept C105795698 @default.
• W2800571504 hasConcept C11413529 @default.
• W2800571504 hasConcept C118615104 @default.
• W2800571504 hasConcept C121332964 @default.
• W2800571504 hasConcept C135692309 @default.
• W2800571504 hasConcept C152877465 @default.
• W2800571504 hasConcept C200695384 @default.
• W2800571504 hasConcept C2524010 @default.
• W2800571504 hasConcept C33923547 @default.
• W2800571504 hasConcept C41008148 @default.
• W2800571504 hasConcept C44280652 @default.

• next