Abstract— In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems. Keywords— Artificial neural networks, Forecasting, Multi-layer perceptron. I. I NTRODUCTION UMEROUS advances have been made in developing intelligent systems, some inspired by biological neural networks, fuzzy systems and combination of them. Researchers from many scientific disciplines are designing Artificial Neural Network (ANNs) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory and control. Conventional approaches have been proposed for solving these problems. Although successful applications can be found in certain well-constrained environments, none is flexible enough to perform well outside its domain. Artificial Neural Network has been replacing traditional methods in many applications offering, besides a better performance, a number of advantages: no need for system model, tolerance bizarre patterns, notable adaptive capability and so on. Load forecasting is one of the most successful applications of ANN in power systems. Neuro-Fuzzy (NF) computing is a popular framework for solving complicated(complex) problems. If we have knowledge expressed in linguistic rules, we can build a Fuzzy Interface System (FIS), and if we have data, or can learn from a simulation (training) then we can use ANNs. For building an FIS, we have to specify the fuzzy sets, fuzzy operators and the knowledge base. Similarly, for constructing an ANN for an application the user needs to specify the architecture and learning algorithm. An analysis reveals that the drawbacks pertaining to these approaches seem to be complementary and therefore it is
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