Disaggregating power consumption of commercial buildings based on the finite mixture model. (1st June 2019)

• Record Type: Journal Article
• Authors: Zhou, Yang; Shi, Zhixiong; Shi, Zhengyu; Gao, Qing; Wu, Libo
• Is Part Of: Applied energy. Volume 243(2019)
• Page Start: 35
• Page End: 46
• Publication Date: 2019-06-01
• Subjects: Energy disaggregation -- Hourly consumption data -- Finite mixture model; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.03.014
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A behavior based disaggregation model is developed to tackle low frequency data. Higher disaggregation accuracy is achieved by multi-factor finite mixture model. Energy disaggregation facilitates demand side management with information provision. Abstract: Power disaggregation that breaks down the overall power consumption to appliance level acts as a feasible technical solution to meet the extensive data demand but reduce the costs of installing advanced metering system in Demand Side Management (DSM). Considering the intensive query of high-frequency training data of existing methods, this paper presents a new behavior based model applicable to low-frequency data by introducing external determining factors of power consumption into a finite mixture model (FMM) that disaggregates overall power consumption into those of various electrical appliances. Empirical verification by employing a dataset including detailed hourly appliance-level power consumption of commercial buildings in Shanghai proves that this newly developed model can provide more accurate result than other previous models but requires relatively lower-frequency data. The benefits of energy-saving potential from information feedback and appliance replacement facilitated by disaggregation data is further simulated to show the practical application.