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 shows the application of the Neighbor Histories (NH) algorithm to the problem of short term electrical load forecasting in a utility company. This algorithm is a simple application of embedding theorems recently used in chaotic time series prediction. The choice of the parameters of the algorithm is usually done manually by trial and error. In this paper the possibility of automatic selection...
Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. In this paper, we will discuss night peak load forecasting of Algerian power system using time series back propagation neural networks, including the effect of the temperature, working days and weekends.
Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. This paper presents the development of a novel fuzzy wavelet neural network model and validates its prediction on the short-term electric load forecasting...
A short-term load forecasting model is adopted with a combined method. The model not only summarizes virtues and defects of neural networks and fuzzy system, but also considers that power system load has characteristics of basic load heft and variability load heft. It uses learned capability of neural networks to complete forecasting work of basic heft for power load. Other effect factors that cause...
The short-term load is nonlinear, and the change of it is influenced by various factors. Be one of them, the temperature is considered the main influencing factor. Not only the temperature of the day to be forecasted take a great influence on the load, but also the temperature of the previous days does. Especially in summer, the influence of the continuous high temperature on the load is different...
The problem of temperature-load relationship revealing is considered. A specialized architecture of a feedforward neural network is proposed that provides separation of temperature influence from other factors and its analysis in an explicit form. The proposed approach is illustrated by computational experiments with data from two locations with different climatic conditions.
Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost...
The paper present the new method of accurate prognosis of the short term load pattern for 24 hours ahead. The method uses the ensemble of neural predictors combined together by applying the blind source separation approach. Thanks to this the less accurate prognoses are not rejected but used to improve the accuracy of the final forecast. The numerical results concerning the prediction of 24-hour pattern...
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.