Recurrent Neural Networks for Short-Term Load Forecasting

An Overview and Comparative Analysis

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Computer Hardware, General Computing
Cover of the book Recurrent Neural Networks for Short-Term Load Forecasting by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi ISBN: 9783319703381
Publisher: Springer International Publishing Publication: November 9, 2017
Imprint: Springer Language: English
Author: Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
ISBN: 9783319703381
Publisher: Springer International Publishing
Publication: November 9, 2017
Imprint: Springer
Language: English

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 methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures.

Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures.

Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.

More books from Springer International Publishing

Cover of the book Collaboration in Creative Design by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Continuum Mechanics through the Ages - From the Renaissance to the Twentieth Century by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book L-Functions and Automorphic Forms by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Parametric Interval Algebraic Systems by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Private Law, Public Law, Metalaw and Public Policy in Space by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Trends in Applications of Mathematics to Mechanics by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Computer Vision – ACCV 2018 by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Control and Prediction of Solid-State of Pharmaceuticals by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Towards Bio-based Flame Retardant Polymers by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Multi Tenancy for Cloud-Based In-Memory Column Databases by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book The Marine Microbiome by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book PET Scan in Hodgkin Lymphoma by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book From Variability Tolerance to Approximate Computing in Parallel Integrated Architectures and Accelerators by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Plasma Cell Neoplasms by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
Cover of the book Distributed, Ambient and Pervasive Interactions: Technologies and Contexts by Enrico Maiorino, Filippo Maria Bianchi, Michael C. Kampffmeyer, Robert Jenssen, Antonello Rizzi
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy