FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2884234184> ?p ?o ?g. }

• W2884234184 endingPage "1020" @default.
• W2884234184 startingPage "1008" @default.
• W2884234184 abstract "Medium-term and long-term energy prediction is essential for the planning and operations of the smart grid eco-system. The prediction of next year and next month energy demand of grid station, independent power producers, commercial, domestic and industrial consumers are allowed administrators to optimize and plan their resources. To address the forecasting problems, the basic intention of this study is to propose an accurate and precise medium and long-term district level energy prediction models employing the machine learning based models which are: 1) artificial neural network with nonlinear autoregressive exogenous multivariable inputs model; 2) multivariate linear regression model; and 3) adaptive boosting model. Based on environmental and aggregated energy consumption data as the model's input and output, the load prediction interval is further classified into three main parts, 1-month ahead forecasting, seasonally ahead forecasting and 1-year ahead forecasting. Feature extraction, data transformation and outlier detection are performed through different data tests. The prediction results intimate that the intended models cannot only increase the forecasting accuracy contrasted with previous forecasting models but also produce adequate forecasting intervals in the smart grid environment. Additionally, these techniques describe an essential step-forward, consolidating the spatiotemporal use of energy inconstancies and variations of district level and strong forecasting capabilities of energy usage requirement in future perceptive." @default.
• W2884234184 created "2018-08-03" @default.
• W2884234184 creator A5054440931 @default.
• W2884234184 creator A5083868707 @default.
• W2884234184 date "2018-10-01" @default.
• W2884234184 modified "2023-10-18" @default.
• W2884234184 title "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment" @default.
• W2884234184 cites W1707584382 @default.
• W2884234184 cites W1790870804 @default.
• W2884234184 cites W1965484410 @default.
• W2884234184 cites W1967410711 @default.
• W2884234184 cites W1971665861 @default.
• W2884234184 cites W1982935541 @default.
• W2884234184 cites W1996828017 @default.
• W2884234184 cites W2011666252 @default.
• W2884234184 cites W2029126275 @default.
• W2884234184 cites W2036776625 @default.
• W2884234184 cites W2037121449 @default.
• W2884234184 cites W2074253301 @default.
• W2884234184 cites W2082235804 @default.
• W2884234184 cites W2087791533 @default.
• W2884234184 cites W2088226074 @default.
• W2884234184 cites W2098207764 @default.
• W2884234184 cites W2123990896 @default.
• W2884234184 cites W2136886086 @default.
• W2884234184 cites W2156302255 @default.
• W2884234184 cites W2165444651 @default.
• W2884234184 cites W2235126960 @default.
• W2884234184 cites W2280154071 @default.
• W2884234184 cites W2436619053 @default.
• W2884234184 cites W2469115065 @default.
• W2884234184 cites W2543909292 @default.
• W2884234184 cites W2546955223 @default.
• W2884234184 cites W2577062852 @default.
• W2884234184 cites W2591614912 @default.
• W2884234184 cites W2593315835 @default.
• W2884234184 cites W2610362680 @default.
• W2884234184 cites W2754029504 @default.
• W2884234184 cites W2756532498 @default.
• W2884234184 cites W2766651241 @default.
• W2884234184 cites W2767124238 @default.
• W2884234184 cites W2772282943 @default.
• W2884234184 cites W2776069847 @default.
• W2884234184 cites W2776741657 @default.
• W2884234184 cites W2790459114 @default.
• W2884234184 cites W2802465900 @default.
• W2884234184 cites W2807115920 @default.
• W2884234184 doi "https://doi.org/10.1016/j.energy.2018.07.084" @default.
• W2884234184 hasPublicationYear "2018" @default.
• W2884234184 type Work @default.
• W2884234184 sameAs 2884234184 @default.
• W2884234184 citedByCount "63" @default.
• W2884234184 countsByYear W28842341842019 @default.
• W2884234184 countsByYear W28842341842020 @default.
• W2884234184 countsByYear W28842341842021 @default.
• W2884234184 countsByYear W28842341842022 @default.
• W2884234184 countsByYear W28842341842023 @default.
• W2884234184 crossrefType "journal-article" @default.
• W2884234184 hasAuthorship W2884234184A5054440931 @default.
• W2884234184 hasAuthorship W2884234184A5083868707 @default.
• W2884234184 hasConcept C10558101 @default.
• W2884234184 hasConcept C105795698 @default.
• W2884234184 hasConcept C119599485 @default.
• W2884234184 hasConcept C119857082 @default.
• W2884234184 hasConcept C121332964 @default.
• W2884234184 hasConcept C124101348 @default.
• W2884234184 hasConcept C127413603 @default.
• W2884234184 hasConcept C149782125 @default.
• W2884234184 hasConcept C151406439 @default.
• W2884234184 hasConcept C154945302 @default.
• W2884234184 hasConcept C159877910 @default.
• W2884234184 hasConcept C161584116 @default.
• W2884234184 hasConcept C162324750 @default.
• W2884234184 hasConcept C186370098 @default.
• W2884234184 hasConcept C187691185 @default.
• W2884234184 hasConcept C193809577 @default.
• W2884234184 hasConcept C24338571 @default.
• W2884234184 hasConcept C2524010 @default.
• W2884234184 hasConcept C2780165032 @default.
• W2884234184 hasConcept C33923547 @default.
• W2884234184 hasConcept C41008148 @default.
• W2884234184 hasConcept C42475967 @default.
• W2884234184 hasConcept C50644808 @default.
• W2884234184 hasConcept C61797465 @default.
• W2884234184 hasConcept C62520636 @default.
• W2884234184 hasConcept C79337645 @default.
• W2884234184 hasConceptScore W2884234184C10558101 @default.
• W2884234184 hasConceptScore W2884234184C105795698 @default.
• W2884234184 hasConceptScore W2884234184C119599485 @default.
• W2884234184 hasConceptScore W2884234184C119857082 @default.
• W2884234184 hasConceptScore W2884234184C121332964 @default.
• W2884234184 hasConceptScore W2884234184C124101348 @default.
• W2884234184 hasConceptScore W2884234184C127413603 @default.
• W2884234184 hasConceptScore W2884234184C149782125 @default.
• W2884234184 hasConceptScore W2884234184C151406439 @default.
• W2884234184 hasConceptScore W2884234184C154945302 @default.
• W2884234184 hasConceptScore W2884234184C159877910 @default.
• W2884234184 hasConceptScore W2884234184C161584116 @default.

• next