Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Assessing Error Bars in Distribution Load Curve Estimation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Acceleration Techniques for the Backpropagation Algorithm
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Confidence interval prediction for neural network models
IEEE Transactions on Neural Networks
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Loads estimation is becoming each time more fundamental for an efficient management and planning of electric distribution systems. Among the factors that contribute to this need of more efficiency are the increasing complexity of these networks, the deregulation process and the competition in an open energy market, and environment preservation requirements. However, the only information generally available at MV and LV levels is essentially of commercial nature, i.e., monthly energy consumption, hired power contracts and activity codes. In consequence, distribution utilities face the problem of estimating load diagrams to be used in planning and operation studies. The typical procedure uses measurements in typical classes of consumers defined by experts to construct inference engines that, most of the times, only estimate peak loads. In this paper, the definition of classes was performed by clustering the collected load diagrams. Artificial Neural Networks (ANN) were then used for load curve estimation. This article describes the adopted methodology and presents some representative results. Performance attained is discussed as well as a method to achieve confidence intervals of the main predicted diagrams.