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 presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic optimization technique called particle swarm optimization (PSO). This training algorithm works to adjust the network weights and biases as to minimize the error function. Wavelet transformed...
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...
Recent researches in load forecasting are quite often based on the use of neural networks in order to predict a specific variable (maximum demand, active electric power or hourly consumption) using past values of the same variable and other exogenous factors proved to influence the value being predicted. This work aims to explore different input patterns in neural networks incorporating information...
In the presented paper by analyzing the curve of the daily electrical network load in Iran over a 10 year period; the effective factors on the daily electricity consumption (including time, environmental and special factors) are studied. Additionally , using the final results from this graphical analysis, a suitable method to train artificial neural networks for short-term forecasting of the time...
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.