| Titre : |
Energy management : big data in power load forecasting |
| Type de document : |
document électronique |
| Auteurs : |
Valentin-A. Boicea, Auteur |
| Editeur : |
Boca Raton : CRC Press, Taylor and Francis Group,LLC |
| Année de publication : |
2021 |
| Collection : |
CRC Focus series |
| Importance : |
1 fichier PDF |
| Présentation : |
ill. |
| ISBN/ISSN/EAN : |
978-1-00-043768-3 |
| Note générale : |
Mode d'accès : accès au texte intégral par :
- authentification après inscription à la plateforme EBSCOhost
ou
- adresse IP de l'École.
Index |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Big data
Power resources--Forecasting
Power resources--Management |
| Index. décimale : |
621.311.1 Réseaux d'approvisionnement du point de vue de la distribution d'énergie. Réseaux et grilles régionaux.Districts d'approvisionnement. Sous stations.Schémas d'électrification. |
| Résumé : |
This book introduces the principle of carrying out a medium-term load forecast (MTLF) at power system level, based on the Big Data concept and Convolutionary Neural Network (CNNs). It also presents further research directions in the field of Deep Learning techniques and Big Data, as well as how these two concepts are used in power engineering.Efficient processing and accuracy of Big Data in the load forecast in power engineering leads to a significant improvement in the consumption pattern of the client and, implicitly, a better consumer awareness. At the same time, new energy services and new lines of business can be developed.The book will be of interest to electrical engineers, power engineers, and energy services professionals. |
| Note de contenu : |
Summary :
1. Introduction
2. General aspects related to the field of big data and big Data in power engineering
3. Big data in power load forecast
4. Conclusions and further research directions |
Energy management : big data in power load forecasting [document électronique] / Valentin-A. Boicea, Auteur . - Boca Raton : CRC Press, Taylor and Francis Group,LLC, 2021 . - 1 fichier PDF : ill.. - ( CRC Focus series) . ISBN : 978-1-00-043768-3 Mode d'accès : accès au texte intégral par :
- authentification après inscription à la plateforme EBSCOhost
ou
- adresse IP de l'École.
Index Langues : Anglais ( eng)
| Mots-clés : |
Big data
Power resources--Forecasting
Power resources--Management |
| Index. décimale : |
621.311.1 Réseaux d'approvisionnement du point de vue de la distribution d'énergie. Réseaux et grilles régionaux.Districts d'approvisionnement. Sous stations.Schémas d'électrification. |
| Résumé : |
This book introduces the principle of carrying out a medium-term load forecast (MTLF) at power system level, based on the Big Data concept and Convolutionary Neural Network (CNNs). It also presents further research directions in the field of Deep Learning techniques and Big Data, as well as how these two concepts are used in power engineering.Efficient processing and accuracy of Big Data in the load forecast in power engineering leads to a significant improvement in the consumption pattern of the client and, implicitly, a better consumer awareness. At the same time, new energy services and new lines of business can be developed.The book will be of interest to electrical engineers, power engineers, and energy services professionals. |
| Note de contenu : |
Summary :
1. Introduction
2. General aspects related to the field of big data and big Data in power engineering
3. Big data in power load forecast
4. Conclusions and further research directions |
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