Recurrent Neural Networks for Short-Term Load Forecasting An Overview and Comparative Analysis /

• Main Authors: Bianchi, Filippo Maria (Author), Maiorino, Enrico (Author), Kampffmeyer, Michael C. (Author), Rizzi, Antonello (Author), Jenssen, Robert (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Series: SpringerBriefs in Computer Science,
• Subjects: Computer science.; Computer software > Reusability.; Computer system failures.; Artificial intelligence.; Power electronics.; Computer Science.; Artificial Intelligence (incl. Robotics).; System Performance and Evaluation.; Power Electronics, Electrical Machines and Networks.; Energy Efficiency.; Performance and Reliability.

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 thu...

Table of Contents:

• Introduction
• Properties and Training in Recurrent Neural Networks
• Recurrent Neural Networks Architectures
• Other Recurrent Neural Networks Models
• Synthetic Time Series
• Real-World Load Time Series
• Experiments
• Conclusions.  .