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With the advent of smart grids, electricity consumers are expected to be enabled to respond to electricity price fluctuations. Thus, forecasting short-term future electricity prices is a key component of optimal demand-side management in an enabled competitive environment. While several price forecasting approaches are available, achieving low forecasting errors is not always possible, especially...
In this paper we present the detection/estimation of weak signals in the presence of unknown chaotic clutter using model based chaotic synchronization. We achieve this task in three steps: (i) First, using Taken's delay embedding theorem and support vector machine (SVM) a model is built from a set of training data, (ii) then a coupled chaotic synchronization scheme is applied to this model to get...
In this paper, we propose a new spatial temporal predictor using support vector machine (SVM) and data fusion technique. SVMs are used as temporal predictors at different spatial domains and spatial temporal prediction is achieved by prediction fusion. Our proposed prediction fusion technique improves the prediction accuracy even in a non-Gaussian environment. The performance of the proposed spatial...
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