Matches in SemOpenAlex for { <https://semopenalex.org/work/W2049622828> ?p ?o ?g. }
- W2049622828 endingPage "2366" @default.
- W2049622828 startingPage "2351" @default.
- W2049622828 abstract "This study presents an integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression (FR) or time series and the integrated algorithm could be an ideal substitute for such cases. At First, preferred Time series model is selected from linear or nonlinear models. For this, after selecting preferred Auto Regression Moving Average (ARMA) model, Mcleod-Li test is applied to determine nonlinearity condition. When, nonlinearity condition is satisfied, the preferred nonlinear model is selected and defined as preferred time series model. At last, the preferred model from fuzzy regression and time series model is selected by the Granger-Newbold. Also, the impact of data preprocessing on the fuzzy regression performance is considered. Monthly electricity consumption of Iran from March 1994 to January 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with other intelligent tools such as Genetic Algorithm (GA) and Artificial Neural Network (ANN)." @default.
- W2049622828 created "2016-06-24" @default.
- W2049622828 creator A5017029449 @default.
- W2049622828 creator A5024383984 @default.
- W2049622828 creator A5050260492 @default.
- W2049622828 date "2010-06-01" @default.
- W2049622828 modified "2023-10-06" @default.
- W2049622828 title "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran" @default.
- W2049622828 cites W1582555728 @default.
- W2049622828 cites W1969681270 @default.
- W2049622828 cites W1973676661 @default.
- W2049622828 cites W1976514586 @default.
- W2049622828 cites W1976662149 @default.
- W2049622828 cites W1981551863 @default.
- W2049622828 cites W1981819350 @default.
- W2049622828 cites W1982348007 @default.
- W2049622828 cites W1984428848 @default.
- W2049622828 cites W1985266527 @default.
- W2049622828 cites W1986156480 @default.
- W2049622828 cites W1995986371 @default.
- W2049622828 cites W1996605950 @default.
- W2049622828 cites W2002899783 @default.
- W2049622828 cites W2003436252 @default.
- W2049622828 cites W2008404566 @default.
- W2049622828 cites W2011227258 @default.
- W2049622828 cites W2016923883 @default.
- W2049622828 cites W2018714386 @default.
- W2049622828 cites W2020610055 @default.
- W2049622828 cites W2021252908 @default.
- W2049622828 cites W2021760340 @default.
- W2049622828 cites W2022349803 @default.
- W2049622828 cites W2023948527 @default.
- W2049622828 cites W2025976911 @default.
- W2049622828 cites W2028844373 @default.
- W2049622828 cites W2030989305 @default.
- W2049622828 cites W2031003331 @default.
- W2049622828 cites W2035272050 @default.
- W2049622828 cites W2035622873 @default.
- W2049622828 cites W2037972590 @default.
- W2049622828 cites W2041398234 @default.
- W2049622828 cites W2041764623 @default.
- W2049622828 cites W2043375348 @default.
- W2049622828 cites W2046307773 @default.
- W2049622828 cites W2046989272 @default.
- W2049622828 cites W2050099778 @default.
- W2049622828 cites W2053865013 @default.
- W2049622828 cites W2054797751 @default.
- W2049622828 cites W2056611606 @default.
- W2049622828 cites W2057160778 @default.
- W2049622828 cites W2057304861 @default.
- W2049622828 cites W2057936307 @default.
- W2049622828 cites W2063952558 @default.
- W2049622828 cites W2066031679 @default.
- W2049622828 cites W2066617434 @default.
- W2049622828 cites W2070338099 @default.
- W2049622828 cites W2085751038 @default.
- W2049622828 cites W2086792939 @default.
- W2049622828 cites W2088503829 @default.
- W2049622828 cites W2088603690 @default.
- W2049622828 cites W2090137850 @default.
- W2049622828 cites W2090791685 @default.
- W2049622828 cites W2092624117 @default.
- W2049622828 cites W2093546394 @default.
- W2049622828 cites W2110603299 @default.
- W2049622828 cites W2113432484 @default.
- W2049622828 cites W2122258997 @default.
- W2049622828 cites W2135827766 @default.
- W2049622828 cites W2136190751 @default.
- W2049622828 cites W2137831894 @default.
- W2049622828 cites W2138026596 @default.
- W2049622828 cites W2144380346 @default.
- W2049622828 cites W2146552111 @default.
- W2049622828 cites W2147096235 @default.
- W2049622828 cites W2162240164 @default.
- W2049622828 cites W2540007442 @default.
- W2049622828 cites W4238919913 @default.
- W2049622828 cites W4243386512 @default.
- W2049622828 doi "https://doi.org/10.1016/j.energy.2009.12.023" @default.
- W2049622828 hasPublicationYear "2010" @default.
- W2049622828 type Work @default.
- W2049622828 sameAs 2049622828 @default.
- W2049622828 citedByCount "107" @default.
- W2049622828 countsByYear W20496228282012 @default.
- W2049622828 countsByYear W20496228282013 @default.
- W2049622828 countsByYear W20496228282014 @default.
- W2049622828 countsByYear W20496228282015 @default.
- W2049622828 countsByYear W20496228282016 @default.
- W2049622828 countsByYear W20496228282017 @default.
- W2049622828 countsByYear W20496228282018 @default.
- W2049622828 countsByYear W20496228282019 @default.
- W2049622828 countsByYear W20496228282020 @default.
- W2049622828 countsByYear W20496228282021 @default.
- W2049622828 countsByYear W20496228282022 @default.
- W2049622828 countsByYear W20496228282023 @default.
- W2049622828 crossrefType "journal-article" @default.
- W2049622828 hasAuthorship W2049622828A5017029449 @default.
- W2049622828 hasAuthorship W2049622828A5024383984 @default.
- W2049622828 hasAuthorship W2049622828A5050260492 @default.