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Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an(More)
This paper proposes the use of two indicators of the predictability of the load series along with an accuracy value such as mean average percentage error as standard measures of load forecasting performance. Over the last 10 years, there has been a significant increase in load forecasting models proposed in engineering journals. Most of these models provide(More)
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(More)
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(More)
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,(More)
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(More)
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(More)
Short-Term Load Forecasting (STLF) has been a relevant research topic for over two decades now. However, it is an ongoing process since the behavior of consumers and producers continue changing as new technologies and new policies become available. This paper presents the results of a research study for the Spanish Transport System Operator (REE) with the(More)
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(More)
The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario can not be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers and(More)