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.
This paper introduced a novel forecasting method, Support Vector Regression with Local Predictor (SVRLP), which aims to forecast the short-term load distribution function. To increase the forecast accuracy, the conventional Support Vector Regression (SVR) is combined with a phase space reconstruction technique, called local predictor. This proposed forecast method can be applied to forecast the load...
This paper presents a novel approach, named the SVR (support vector regression) based SVRLP (support vector regression local predictor) with FNF-SVRLP (false neighbours filtered-support vector regression local predictor), to predict short-term natural gas demand. This method integrates the SVR algorithm with the reconstruction properties of a time series, and optimises the original local predictor...
With the development of the wind power, the prediction of wind speed has received much attention. In this paper, the support vector regression based local predictor (SVRLP) is combined with mathematical morphology, named as SVRLP-MM, and applied to the short-term wind speed prediction. Through the mathematical morphology, the wind speed time series would be decomposed into two subsequences, named...
Wind power prediction has received much attention due to the development renewable energy sources using wind power. The paper presents a new approach which is a support vector regression (SVR) based local predictor (LP) with false neighbours filtered (FNF-SVRLP) to undertake short-term wind power perdition. The proposed predication method not only combines the powerful SVR with the reconstruction...
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.