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

ISBN-13: 9783319703374 / Angielski / Miękka / 2017 / 72 str.

Filippo Maria Bianchi; Enrico Maiorino; Michael C. Kampffmeyer

Opis

The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series.

Spis treści

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

O autorze

Dr. Filippo Maria Bianchi is a postdoctoral researcher in the Department of Physics and Technology at the Arctic University of Norway, Tromsø, Norway. Dr. Michael C. Kampffmeyer is a research fellow at the same institution. Dr. Robert Jenssen is an associate professor at the same institution. Dr.  Enrico Maiorino is a research fellow in the Channing Division of Network Medicine at Harvard Medical School, Boston, MA, USA. Dr. Antonello Rizzi is an assistant professor in the Department of Information Engineering, Electronics and Telecommunications at the Sapienza University of Rome, Italy.