Non-intrusive Load Monitoring

Theory, Technologies and Applications

Häftad, Engelska, 2021

AvHui Liu

1 389 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

Finns i fler format (1)

Inbunden

1 389 kr

Skickas inom 10-15 vardagar

Focusing on non-intrusive load monitoring techniques in the area of smart grids and smart buildings, this book presents a thorough introduction to related basic principles, while also proposing improvements.

Produktinformation

Utgivningsdatum2021-01-02
Mått155 x 235 x 16 mm
Vikt446 g
FormatHäftad
SpråkEngelska
Antal sidor277
FörlagSpringer Verlag, Singapore
ISBN9789811518621

Tillhör följande kategorier

Energiteknik inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT

Professor Hui Liu holds double Ph.D. degrees from both China and Germany. He served as the BMBF Junior Group Leader for Germany’s Ministry of Education & Research at the Institute of Automation, Faculty of Computer Science & Electrical Engineering, University of Rostock, Germany, from 2015 to 2016.He has published more than 50 international research papers in the fields of smart energy and green cities and holds 40 related patents as the first inventor. He has won many major research awards, including the Natural Science Research Award from the Ministry of Education of China in 2017 (second prize), the National Scientific and Technological Progress Award of China (first prize) in 2018, and the Scientific and Technology Award of the China Railway Society (first prize) in 2018. He currently serves as an associate editor of the journal Transportation Safety& Environment (Oxford University Press) and as a committee member for more than 20 international academic conferences.

Introduction.- Detection of Transient Events in Time Series.- Appliance Signature Extraction.- Appliance Identification Based on Template Matching.- Steady State Current Decomposition Based Appliance Identification.- Machine Learning Based Appliance Identification.- Hidden Markov Models Based Appliance Identification.- Deep Learning Based Appliance Identification.- Deterministic Prediction of Electric Load Time Series.- Interval Prediction of Electric Load Time Series.