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This paper presents a methodology for short-term load forecasting using a semigroup-based system-type neural network. A technique referred to as algebraic decomposition is proposed for the neural network architecture, where the network is decomposed into a semigroup channel and a function channel. The semigroup channel, made of coefficient vector, is shown to exhibit the dependency of the load on...
This paper presents a methodology for long-term electric power demands using a semigroup based system-type neural network architecture. The assumption is that given enough data, the next year's loads can be predicted using only components from the previous few years. This methodology is applied to recent load data, and the next year's load data is satisfactorily forecasted. This method also provides...
This paper presents a new approach for short term load forecasting using a diagonal recurrent neural network with an adaptive learning rate. The fully connected recurrent neural network (FRNN), where all neurons are coupled to one another, is difficult to train and to converge in a short time. The DRNN is a modified model of FRNN. It requires fewer weights than FRNN and rapid convergence has been...
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