The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, 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, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static.
Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis (SpringerBriefs in Computer Science) books pdf online
Filippo Maria Bianchi Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis (SpringerBriefs in Computer Science) free
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