Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis

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

Maiorino, Enrico
Kampffmeyer, Michael C.
Bianchi, Filippo Maria

Paperback

Series: Springerbriefs in Computer Science

Technology & EngineeringGeneral Computers

ISBN10: 3319703374
ISBN13: 9783319703374
Publisher: Springer Nature
Published: Nov 17 2017
Pages: 72
Weight: 0.29
Height: 0.17 Width: 6.14 Depth: 9.21
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.

Also in

General Computers

View All