The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In order to improve the forecasting accuracy for clean energy consumption with inherently high complexity, a hybrid learning paradigm integrating genetic algorithm (GA) and least squares support vector regression (LSSVR), i.e., GA-LSSVR model, is formulated in this study. In this learning paradigm, LSSVR, as a powerful artificial intelligence tool, is employed to forecast clean energy consumption,...
To improve the prediction accuracy of crude oil price even in current complicated international situation, this paper proposed a novel model linking firefly algorithm (FA) with least squares support vector regression (LSSVR), namely FA-LSSVR. In this hybrid intelligent model, FA is used to find the optimal values of LSSVR parameters (i.e., penalty coefficient and kernel function parameters), in order...
From the perspective of energy security, this paper focuses on country risk forecasting for major oil exporting countries. Due to the two main characteristics of country risk of oil exporting countries, i.e. the complexity and the mutability, this study proposes a decomposition hybrid approach (DHA) for predicting country risk of oil exporters, based on the principle of “decomposition and ensemble”...
Based on the principle of "decomposition and ensemble" and strategy of "the divide and conquer" [1,2], a hybrid Methodology integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting. In the proposed EEMD-LSSVR-based Decomposition-and-Ensemble Methodology, the EEMD is first...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.