Author: Filippo Maria Bianchi
Publisher: Springer
ISBN: 3319703382
Category : Computers
Languages : en
Pages : 74
Book Description
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.
Recurrent Neural Networks for Short-Term Load Forecasting
Author: Filippo Maria Bianchi
Publisher: Springer
ISBN: 3319703382
Category : Computers
Languages : en
Pages : 74
Book Description
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.
Publisher: Springer
ISBN: 3319703382
Category : Computers
Languages : en
Pages : 74
Book Description
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.
Short Term Load Forecasting Using Artificial Neural Networks
Author: Syed Mahmood Ahmed
Publisher:
ISBN:
Category : Electric power consumption
Languages : en
Pages : 134
Book Description
Publisher:
ISBN:
Category : Electric power consumption
Languages : en
Pages : 134
Book Description
Short Term Load Forecasting Using Artificial Neural Networks
Author: Andrew Rae
Publisher:
ISBN:
Category :
Languages : en
Pages : 132
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 132
Book Description
Short-term Load Forecasting Using Artificial Neural Networks
Author: Marjanossadat Mortazavi Naeini
Publisher:
ISBN:
Category : Electric power consumption
Languages : en
Pages : 125
Book Description
Publisher:
ISBN:
Category : Electric power consumption
Languages : en
Pages : 125
Book Description
Intelligent Systems'2014
Author: D. Filev
Publisher: Springer
ISBN: 3319113100
Category : Technology & Engineering
Languages : en
Pages : 893
Book Description
This two volume set of books constitutes the proceedings of the 2014 7th IEEE International Conference Intelligent Systems (IS), or IEEE IS’2014 for short, held on September 24‐26, 2014 in Warsaw, Poland. Moreover, it contains some selected papers from the collocated IWIFSGN'2014-Thirteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets.The conference was organized by the Systems Research Institute, Polish Academy of Sciences, Department IV of Engineering Sciences, Polish Academy of Sciences, and Industrial Institute of Automation and Measurements - PIAP.The papers included in the two proceedings volumes have been subject to a thorough review process by three highly qualified peer reviewers.Comments and suggestions from them have considerable helped improve the quality of the papers but also the division of the volumes into parts, and assignment of the papers to the best suited parts.
Publisher: Springer
ISBN: 3319113100
Category : Technology & Engineering
Languages : en
Pages : 893
Book Description
This two volume set of books constitutes the proceedings of the 2014 7th IEEE International Conference Intelligent Systems (IS), or IEEE IS’2014 for short, held on September 24‐26, 2014 in Warsaw, Poland. Moreover, it contains some selected papers from the collocated IWIFSGN'2014-Thirteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets.The conference was organized by the Systems Research Institute, Polish Academy of Sciences, Department IV of Engineering Sciences, Polish Academy of Sciences, and Industrial Institute of Automation and Measurements - PIAP.The papers included in the two proceedings volumes have been subject to a thorough review process by three highly qualified peer reviewers.Comments and suggestions from them have considerable helped improve the quality of the papers but also the division of the volumes into parts, and assignment of the papers to the best suited parts.
Short Term Load Forecasting Using Artificial Neural Network
Author: Lawerence Chong
Publisher:
ISBN:
Category : Electric networks
Languages : en
Pages : 224
Book Description
Publisher:
ISBN:
Category : Electric networks
Languages : en
Pages : 224
Book Description
Power System Peak Load Forecasting Using an Artifical Neural Network
Author: John Cass
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 278
Book Description
Examines several models for short term load forecasting for the Rockhampton Area during the first six months of 1995. Artificial Neural Networks (ANN) were used.
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 278
Book Description
Examines several models for short term load forecasting for the Rockhampton Area during the first six months of 1995. Artificial Neural Networks (ANN) were used.
Neural Networks for Pattern Recognition
Author: Christopher M. Bishop
Publisher: Oxford University Press
ISBN: 0198538642
Category : Computers
Languages : en
Pages : 501
Book Description
Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Publisher: Oxford University Press
ISBN: 0198538642
Category : Computers
Languages : en
Pages : 501
Book Description
Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Short-Term Load Forecasting by Artificial Intelligent Technologies
Author: Wei-Chiang Hong
Publisher: MDPI
ISBN: 3038975826
Category :
Languages : en
Pages : 445
Book Description
This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Publisher: MDPI
ISBN: 3038975826
Category :
Languages : en
Pages : 445
Book Description
This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Short Term Load Forecasting Using Artificial Neural Networks
Author: Iskandar Kfoury
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 45
Book Description
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 45
Book Description