Residential energy flexibility characterization using non-intrusive load monitoring. (December 2021)

• Record Type: Journal Article
• Authors: Azizi, Elnaz; Ahmadiahangar, Roya; Rosin, Argo; Martins, Joao; Lopes, Rui Amaral; Beheshti, M.TH.; Bolouki, Sadegh
• Is Part Of: Sustainable cities and society. Volume 75(2021)
• Publication Date: 2021-12
• Subjects: Event detection -- Energy flexibility -- Non-intrusive load monitoring; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103321
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Utilization of a novel unsupervised NILM method to disaggregate the consumption signal of shiftable appliances from the total consumption signal. The earliest and latest start-time of appliances is determined based on consumers' usage behavior and utilized in EF characterization. The similarity of the extracted EF pattern in an individual building level is more than 90% and in the aggregated level the EF characterization is improved more than 40% based on the proposed method. Abstract: To accelerate progress in building sustainability as well as to aid balance supply and demand in the presence of renewable energy generation, a tailored characterization method for the energy flexibility (EF) of buildings is needed. In this paper, a novel two-stage non-intrusive EF characterization method is proposed. In the first stage, unlike the previous studies in which an individual meter is installed on appliances to extract their consumption pattern, a novel unsupervised event-matching non-intrusive load monitoring method is utilized which is time and cost-effective. Moreover, previous research characterize the EF considering as early and as late as possible appliances' start-time. However, the usage behavior of consumers affects the start-time of appliances. To tackle this issue, in the second stage of the proposed method, the usage behavior of consumers is taken into account for the EF characterization. The proposed method is verified in an individual building level and aggregated level including 50 residential buildings. The obtained results show that the proposed usage behavior-oriented method, characterizes the available aggregated EF with higher accuracy, without adding complexity to the system. These results can be used by aggregators to harness the available EF of buildings to flatten demand consumption by incentivizing potential consumers.