Titlebook: Recurrent Neural Networks for Short-Term Load Forecasting; An Overview and Comp Filippo Maria Bianchi,Enrico Maiorino,Robert Jenss Book 201

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書目名稱Recurrent Neural Networks for Short-Term Load Forecasting讀者反饋學科排名

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Synthetic Time Series,k architectures in a controlled environment. The generative models of the synthetic time series are the Mackey–Glass system, NARMA, and multiple superimposed oscillators.Those are benchmark tasks commonly considered in the literature to evaluate the performance of a predictive model. The three forec

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Experiments,th the synthetic tasks and the real-world datasets. For each architecture, we report the optimal configuration of its hyperparameters for the task at hand, and the best learning strategy adopted for training the model weights. We perform several independent evaluation of the prediction results due t

Ruptured-Disk

Conclusions,ferent results and performance achieved by the Recurrent Neural Network architectures analyzed. We conclude by hypothesizing possible guidlines for selecting suitable models depending on the specific task at hand.

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Book 2017, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system...Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models,

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