Energy disaggregation, or nonintrusive load monitoring (NILM), aims at estimating the power demand of individual appliances from a household's aggregate electricity consumption. Due to the notable rise in the number of installed smart meters and owing to the numerous advantages of this approach over intrusive methods, NILM has received growing attention in the recent years. In this chapter, after reviewing different categories of household appliances, the state-of-the-art load signatures, including both macroscopic and microscopic features, are introduced. Next, commonly used supervised and unsupervised disaggregation algorithms, which are employed to classify the appliances based on the extracted features, are discussed. Publically accessible datasets and open-source tools, which have been released in the recent years to assist the NILM research and to facilitate the comparison of disaggregation algorithms, are then reviewed. Finally, main applications of energy disaggregation, including providing itemized energy bills, enabling more accurate demand prediction, identifying mal-functioning appliances, and assisting occupancy monitoring, are presented.

Data analytics for energy disaggregation: methods and applications

B. Najafi;S. Moaveninejad;F. Rinaldi
2017-01-01

Abstract

Energy disaggregation, or nonintrusive load monitoring (NILM), aims at estimating the power demand of individual appliances from a household's aggregate electricity consumption. Due to the notable rise in the number of installed smart meters and owing to the numerous advantages of this approach over intrusive methods, NILM has received growing attention in the recent years. In this chapter, after reviewing different categories of household appliances, the state-of-the-art load signatures, including both macroscopic and microscopic features, are introduced. Next, commonly used supervised and unsupervised disaggregation algorithms, which are employed to classify the appliances based on the extracted features, are discussed. Publically accessible datasets and open-source tools, which have been released in the recent years to assist the NILM research and to facilitate the comparison of disaggregation algorithms, are then reviewed. Finally, main applications of energy disaggregation, including providing itemized energy bills, enabling more accurate demand prediction, identifying mal-functioning appliances, and assisting occupancy monitoring, are presented.
2017
Big data application in power systems
978-012811969-3
Energy disaggregation, energy savings, machine learning, non intrusive load monitoring, smart meter.
File in questo prodotto:
File Dimensione Formato  
Big Data Applications in Power Systems Chapter 17 Data analystics for energy disaggregation methods and applications.pdf

Accesso riservato

Descrizione: Big Data Applications in Power Systems Chapter 17 Data analytics for energy disaggregation methods and applications
: Publisher’s version
Dimensione 375.77 kB
Formato Adobe PDF
375.77 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1048888
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 38
  • ???jsp.display-item.citation.isi??? ND
social impact