Recurrent neural networks for short-term load forecasting : an overview and comparative analysis /: an overview and comparative analysis. (2017)
- Record Type:
- Book
- Title:
- Recurrent neural networks for short-term load forecasting : an overview and comparative analysis /: an overview and comparative analysis. (2017)
- Main Title:
- Recurrent neural networks for short-term load forecasting : an overview and comparative analysis
- Further Information:
- Note: Filippo Maria Bianchi [and four others].
- Authors:
- Bianchi, Filippo Maria
- Contents:
- IntroductionProperties and Training in Recurrent Neural NetworksRecurrent Neural Networks ArchitecturesOther Recurrent Neural Networks ModelsSynthetic Time SeriesReal-World Load Time SeriesExperimentsConclusions.
- Publisher Details:
- Cham : Springer
- Publication Date:
- 2017
- Copyright Date:
- 2017
- Extent:
- 1 online resource (72 pages)
- Subjects:
- 006.3/2
Computer science
Neural networks (Computer science)
Neural networks (Computer science)
Computers -- Hardware -- Handheld Devices
Technology & Engineering -- Machinery
Technology & Engineering -- Power Resources -- General
Computers -- Hardware -- General
Systems analysis & design
Power networks, systems, stations & plants
Energy technology & engineering
Artificial intelligence
Computer system performance
Production of electric energy or
Operating systems (Computers)
Computers -- Intelligence (AI) & Semantics
Artificial intelligence - Languages:
- English
- ISBNs:
- 9783319703381
- Related ISBNs:
- 9783319703374
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.348068
- Ingest File:
- 01_302.xml