Energy Management

Big Data in Power Load Forecasting
1. Auflage 2024
ISBN: 978-0-367-70662-3
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 92 Seiten, Format (B × H): 140 mm x 216 mm, Gewicht: 118 g

Reihe: CRC Press Focus Shortform Book Program

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.

Zielgruppe

Academic and Professional

Autoren/Hrsg.

Boicea, Valentin A

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Naturwissenschaften Physik Mechanik Energie

Inhaltsverzeichnis

1. Big Data Analysis Tools: Data Collection and Sampling. 2. Big Data and the Energy Field. 3. The Load Forecast: A New Application for Big Data. 4. Conclusions.

Über Autor(innen)

Adrian–Valentin Boicea, a former PhD student at Politecnico di Torino, Italy, received the BS in electrical engineering and electrical power systems from the University Politehnica of Bucharest (UPB), Romania. Currently, he is a Lecturer within the Department of Electrical Power Systems at the UPB. His research interests include the distributed generation systems, energy efficiency, renewable sources, the operational research algorithms used in power engineering, as well as Big Data analysis applied in the energy sector.