Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In order to efficiently improve the prediction accuracy by selecting input variables and the training pattern, a load forecasting model based on data mining technique is presented. The model consists of three stages: firstly, the rough set theory and the genetic algorithm are applied to find relevant factors to the load; secondly, the active selection of the training pattern is carried out; last,...
The short-term load forecasting model based on neural network has been applied widely in energy management systems (EMS) because of its high forecasting accuracy and self-learning ability. But the forecasting errors of the load curve near peaks are large, especially at the large slope difference on both side of a peak. So the load forecasting based on rough set and neural network is proposed. The...
Support vector machines (SVM) has been used in load forecasting field. The noise and redundancy of sample data are important factors to the generalized performance of SVM. They can cause some disadvantages of slow convergence speed and low forecasting accuracy. A SVM forecasting method for short-term load forecasting based on rough sets (RS-SVM) is developed in this paper, using rough sets algorithm...
This paper aims for developing a method, based on rough set (RS) reduction and wavelet neural network (WNN), to improve the efficiency of short-term load forecasting (STLF). The RS reduction could erase redundant characters and this makes it possible to take many influential factors of power load into account, although the learning ability of neural network is limited. Furthermore, WNN is brought...
This paper aims to develop a load forecasting method for short-term load forecasting based on a hybrid approach, which combines the support vector regression method and the rough sets method. In the first stage, the rough sets method is applied to reduce the redundant attributes among varied factors that affect the short-term load forecasting. Then, a SVR module is trained using historical data reconstructed...
Short-term load forecasting has always been the essential part of reliable and economic operation in power systems. In this paper, a hybrid neural network model combining rough set theory, principal component analysis, dynamic clustering analysis and ant colony optimization algorithm is presented. First, rough set theory is used to eliminate redundant influential factors that don't exert tremendous...
In this paper a reduction algorithm based on rough set theory is presented due to too much factors that influence accuracy in the power load forecasting. The reduction algorithm introduced to mine more correlative attributes in the pending forecasting components, ensures not only the rationality of input parameters of forecasting model but also the selection of input parameters of ANN model. An RAPHF...
This paper presents an approach based on rough set. The approach improves case-based reasoning to reduce the initial information and to find similar historical daily information. The result of case-based reasoning will be put into an artificial neural network to process and then get the forecasting result. The paper provides a new method to selecting a relevant feature subset and feature weights....
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.