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.
Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation,...
An accurate and efficient Short-Term Load Forecasting (STLF) plays a vital role for economic operational planning of both regulated power systems and electricity markets. Therefore, many techniques and approaches for STLF problem have been presented in the literature. However, there is still an essential need to develop more accurate load forecast method. This paper presents the application of artificial...
One of the basic requirements for power systems is accurate short-term load forecasting (STLF). In this study, the application of artificial neural networks is explored for designing of short-term load forecasting systems for electricity market of Iran. In this paper, two seasonal artificial neural networks (ANNs) are designed and compared; so that model 2 (hourly load forecasting model) is partitioning...
The STLF algorithms belong to the set of methodologies which aim to furnish more effectiveness in planning, operation and conduction in electric energy systems. Actions like, maintenance issues, network management, and eventual power purchase decisions within liberalized electricity markets require, among others, reliable next-hour load forecasts. Regressive methods are widely used. Artificial neural...
Power is important to modern society and national economy. To forecast short-term load more accurately, phase space of the complex nonlinear system was reestablished according to chaos theory and properties of short-term load were analyzed. It proves that forecasting short-term load is a classic decision-making process, full of chaos. Combining with chaos theory and traditional BP network, an improved...
This paper present a comparative study between ANFIS, neuro-fuzzy (NF) and artificial neural network (ANN) approaches, applied to STLF algorithm (one hour ahead). Distribution networks need reliable short-term load forecast. The STLF algorithms associated with network management, as load dispatch and network reconfiguration, under quality of service constraints, improves the maintenance issues and...
Since year 2000, the increase of the installed wind energy capacity all over the world (mainly in Europe and United States) attracted the attention of electricity companies, wind farm promoters and researchers towards the short term prediction, mainly motivated by the necessity of integration into the grid of an increasing dasiaunknownpsila (fluctuating) amount of wind power. Besides, in a deregulated...
Ancillary services (AS) are essential for secure, stable and economical operation of the power system. Moreover, AS plays a vital role in free and fair trade of electricity in emerging competitive power market. Hence, the AS procurement is a major operational function for the independent system operator (ISO) in the electricity market. Spinning reserve (SR) is one of the most important AS required...
Load demand prediction is vital for maintaining stability and controlling risks of electricity market. An improved model which combines neural network with genetic algorithm is proposed to accurately predict load demand at equilibrium situation of day-ahead electricity market. In the proposed model, load demand prediction problem is converted into optimization problem of error minimization between...
Long-term load forecasting has a vital role in generation, transmission and distribution network planning. Traditional studies for long-term load forecasting were based on regression method, which could not provide a true representation of power system behavior in a volatile electricity market. The purpose of this paper is to introduce two approaches based regression method and artificial neural network...
This paper describes a short time electrical energy demand forecast system using two different techniques of artificial intelligence: recurrent artificial neural networks and support vector regression. A brief analysis of the demand over the electrical energy network connection points is also done.
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.