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 an application of linear mixed models to short-term load forecasting. The starting point of the design is a currently working model at the Spanish Transport System Operator, which is based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load...
The classification of an annual time series by using data from past years is investigated in this letter. Several classification schemes based on data fusion, sparse learning, and semisupervised learning are proposed to address the problem. Numerical experiments are performed on a Moderate Resolution Imaging Spectroradiometer image time series and show that while several approaches have statistically...
There has been a significant production of load forecasting models over the last 5 years. These models present a wide variety of techniques, most of them using novel artificial intelligence approaches. Load forecasting is a complex matter and it is the result of several processes that, depending on the database, may be of more or less importance. However, most models focus their attention only on...
An artificial neural network based on Kohonen self-organizing maps (SOM) and its application to short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting up to 24 hour long profiles, up to 24 hours ahead of the beginning of the period. The input used by the model depends on the available information at the time of the forecast, and it may contain meteorological...
The study presented in this paper used Kohonen's Self-Organized Maps, which is one of the more uncommon techniques based on neural networks in load forecasting. The aim of this study is not only to show that this technique is capable of producing accurate short-term load forecasting results which should not be neglected, but also to provide a deep and thorough analysis of these results in order to...
The objective of this research is to analyze the capacity of the Multilayer Perceptron Neural Network (MLP) versus Self-Organizing Map Neural Network (SOM) for Short-Term Load Forecasting. The MLP is one of the most commonly used networks. It can be used for classification problems, model construction, series forecasting and discrete control. For the forecasting problems, a backpropagation (BP) algorithm...
Short-term forecasting is required by utility planners and electric system operators for tactical operational planning and day-to-day decision making. The forecasting is intended to obtain the system load demand over a period of hours or days, and it plays an important role in determining unit commitment, spinning reserve, economic power interchange, load management etc… Electrical load has a time-varying...
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.