FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2752072929> ?p ?o ?g. }

• W2752072929 endingPage "1004" @default.
• W2752072929 startingPage "988" @default.
• W2752072929 abstract "Forecasting methods are one of the most efficient available approaches to make managerial decisions in various fields of science. Forecasting is a powerful approach in the planning process, policy choices and economic performance. The accuracy of forecasting is an important factor affects the quality of decisions that generally has a direct non-strict relationship with the decisions quality. This is the most important reason that why the endeavor for enhancement the forecasting accuracy has never been stopped in the literature. Electricity load forecasting is one of the most challenging areas forecasting and important factors in the management of energy systems and economic performance. Determining the level of the electricity load is essential for precise planning and implementation of the necessary policies. For this reason electricity load forecasting is important for financial and operational managers of electricity distribution. The unique feature of the electricity which makes it more difficult for forecasting in comparison with other commodities is the impossibility of storing it in order to use in the future. In other words, the production and consumption of electricity should be taken simultaneously. It has caused to create a high level of complexity and ambiguity in electricity markets. Computational intelligence and soft computing approaches are among the most precise and useful approaches for modeling the complexity and uncertainty in data, respectively. In the literature, several hybrid models have been developed in order to simultaneously use unique advantages of these models. However, iterative suboptimal meta-heuristic based models are always used for combining in these models. In this paper, a direct optimum parallel hybrid (DOPH) model is proposed based on multilayer perceptrons (MLP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS), and Seasonal Autoregressive Integrated Moving Average (SARIMA) in order to electricity load forecasting. The main idea of the proposed model is to simultaneously use advantages of these models in modeling complex and ambiguous systems in a direct and optimal structure. It can be theoretically demonstrated that the proposed model due to use the direct optimal structure, can achieve non-less accuracy than iterative suboptimal hybrid models, while its computational costs are significantly lower than those hybrid models. Empirical results indicate that the proposed model can achieve more accurate results rather than its component and some other seasonal hybrid models." @default.
• W2752072929 created "2017-09-15" @default.
• W2752072929 creator A5005829949 @default.
• W2752072929 creator A5074298629 @default.
• W2752072929 date "2017-12-01" @default.
• W2752072929 modified "2023-10-06" @default.
• W2752072929 title "A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting" @default.
• W2752072929 cites W1976611654 @default.
• W2752072929 cites W1976754765 @default.
• W2752072929 cites W1983184804 @default.
• W2752072929 cites W1986604238 @default.
• W2752072929 cites W1986975877 @default.
• W2752072929 cites W1991667190 @default.
• W2752072929 cites W2005683380 @default.
• W2752072929 cites W2005747371 @default.
• W2752072929 cites W2011227258 @default.
• W2752072929 cites W2016649933 @default.
• W2752072929 cites W2019207321 @default.
• W2752072929 cites W2020246210 @default.
• W2752072929 cites W2035737123 @default.
• W2752072929 cites W2047159855 @default.
• W2752072929 cites W2051502925 @default.
• W2752072929 cites W2055485811 @default.
• W2752072929 cites W2056043406 @default.
• W2752072929 cites W2058326618 @default.
• W2752072929 cites W2061152874 @default.
• W2752072929 cites W2062901109 @default.
• W2752072929 cites W2068438324 @default.
• W2752072929 cites W2070190840 @default.
• W2752072929 cites W2072690957 @default.
• W2752072929 cites W2083130827 @default.
• W2752072929 cites W2097314007 @default.
• W2752072929 cites W2117014758 @default.
• W2752072929 cites W2125644289 @default.
• W2752072929 cites W2135827766 @default.
• W2752072929 cites W2145777288 @default.
• W2752072929 cites W2189919008 @default.
• W2752072929 cites W2340590753 @default.
• W2752072929 cites W2503000058 @default.
• W2752072929 cites W2523477444 @default.
• W2752072929 cites W2591426394 @default.
• W2752072929 cites W3125462345 @default.
• W2752072929 cites W2127457178 @default.
• W2752072929 doi "https://doi.org/10.1016/j.energy.2017.09.009" @default.
• W2752072929 hasPublicationYear "2017" @default.
• W2752072929 type Work @default.
• W2752072929 sameAs 2752072929 @default.
• W2752072929 citedByCount "49" @default.
• W2752072929 countsByYear W27520729292018 @default.
• W2752072929 countsByYear W27520729292019 @default.
• W2752072929 countsByYear W27520729292020 @default.
• W2752072929 countsByYear W27520729292021 @default.
• W2752072929 countsByYear W27520729292022 @default.
• W2752072929 countsByYear W27520729292023 @default.
• W2752072929 crossrefType "journal-article" @default.
• W2752072929 hasAuthorship W2752072929A5005829949 @default.
• W2752072929 hasAuthorship W2752072929A5074298629 @default.
• W2752072929 hasConcept C10138342 @default.
• W2752072929 hasConcept C111472728 @default.
• W2752072929 hasConcept C111919701 @default.
• W2752072929 hasConcept C112930515 @default.
• W2752072929 hasConcept C119599485 @default.
• W2752072929 hasConcept C127413603 @default.
• W2752072929 hasConcept C13736549 @default.
• W2752072929 hasConcept C138885662 @default.
• W2752072929 hasConcept C144133560 @default.
• W2752072929 hasConcept C146733006 @default.
• W2752072929 hasConcept C154945302 @default.
• W2752072929 hasConcept C162324750 @default.
• W2752072929 hasConcept C173801870 @default.
• W2752072929 hasConcept C182306322 @default.
• W2752072929 hasConcept C193809577 @default.
• W2752072929 hasConcept C199360897 @default.
• W2752072929 hasConcept C206658404 @default.
• W2752072929 hasConcept C2779530757 @default.
• W2752072929 hasConcept C2780522230 @default.
• W2752072929 hasConcept C2781104810 @default.
• W2752072929 hasConcept C41008148 @default.
• W2752072929 hasConcept C42475967 @default.
• W2752072929 hasConcept C98045186 @default.
• W2752072929 hasConceptScore W2752072929C10138342 @default.
• W2752072929 hasConceptScore W2752072929C111472728 @default.
• W2752072929 hasConceptScore W2752072929C111919701 @default.
• W2752072929 hasConceptScore W2752072929C112930515 @default.
• W2752072929 hasConceptScore W2752072929C119599485 @default.
• W2752072929 hasConceptScore W2752072929C127413603 @default.
• W2752072929 hasConceptScore W2752072929C13736549 @default.
• W2752072929 hasConceptScore W2752072929C138885662 @default.
• W2752072929 hasConceptScore W2752072929C144133560 @default.
• W2752072929 hasConceptScore W2752072929C146733006 @default.
• W2752072929 hasConceptScore W2752072929C154945302 @default.
• W2752072929 hasConceptScore W2752072929C162324750 @default.
• W2752072929 hasConceptScore W2752072929C173801870 @default.
• W2752072929 hasConceptScore W2752072929C182306322 @default.
• W2752072929 hasConceptScore W2752072929C193809577 @default.
• W2752072929 hasConceptScore W2752072929C199360897 @default.
• W2752072929 hasConceptScore W2752072929C206658404 @default.
• W2752072929 hasConceptScore W2752072929C2779530757 @default.

• next