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We understand a bit better about how they're determined so that we can make better, more intelligent forecasts of where they're headed.
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This is the first study that presents an integrated intelligent forecasting approach for accurate gas consumption considering the economic, environmental and IT/IS indicators.
Meanwhile, increasing worldwide demand for different types of energy requires development of advanced intelligent forecasting tools to provide a basis from which decisions and plans can be made.
We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies.
Get intelligent intelligence.
This paper develops and investigates an ensemble learning based multistage intelligent approach to forecast 5 days global horizontal radiation at four given locations of India.
This study presents an adaptive intelligent algorithm for forecasting gasoline demand based of artificial neural network (ANN), conventional regression and design of experiment (DOE).
In this work, the model based on a feed-forward artificial neural network (ANN) optimized by hybrid genetic algorithm and particle swarm optimization (HGAPSO) as an intelligent approach to forecast asphaltene precipitation due natural depletion is proposed.
The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.
The findings could be used to help develop traffic forecasting and intelligent transport systems, Kerner says.
This work will further continue into the development of fault forecasting using intelligent computational methods, such as Genetic Algorithms (GA), Fuzzy Logic, and Artificial Neural Network.
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