Energy Management - Big Data in Power Load Forecasting

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 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.

List of contents

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.

About the author

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.

Summary

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 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.

Product details

• Authors: Valentin A. Boicea, Boicea Valentin A.
• Publisher: Taylor and Francis
• Languages: English
• Product format: Paperback / Softback
• Released: 08.10.2024
• EAN: 9780367706623
• ISBN: 978-0-367-70662-3
• No. of pages: 82
• Weight: 200 g
• Illustrations: schwarz-weiss Illustrationen, Zeichnungen, schwarz-weiss
• Series: CRC Press Focus Shortform Book Program
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; TECHNOLOGY & ENGINEERING / Mechanical, ENERGY, SCIENCE / Energy, TECHNOLOGY & ENGINEERING / Power Resources / Electrical, COMPUTERS / Machine Theory, Energy technology & engineering, Mathematical modelling, Image processing, Automatic control engineering, Databases, Energy technology and engineering, Instruments & instrumentation engineering, Neural networks and fuzzy systems, Neural networks & fuzzy systems, Instruments and instrumentation