A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings. (15th June 2021)

• Record Type: Journal Article
• Authors: Brucke, Karoline; Arens, Stefan; Telle, Jan-Simon; Steens, Thomas; Hanke, Benedikt; von Maydell, Karsten; Agert, Carsten
• Is Part Of: Applied energy. Volume 292(2021)
• Publication Date: 2021-06-15
• Subjects: Non-intrusive load monitoring -- Energy disaggregation -- Power prediction -- Unsupervised learning -- Neural networks; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.116860
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: In this study, a new algorithm is developed to extract device profiles in a fully unsupervised manner from three-phases reactive and active aggregate power measurements. The extracted device profiles are then applied to disaggregate the aggregate power measurements by means of particle swarm optimization. Then, a new approach to very short-term power predictions is presented, which makes use of the disaggregation data. For this purpose, a state change forecast is carried out for each device by an artificial neural network and subsequently converted into a power prediction by reconstructing the power profile with respect to the state changes and device profiles. The forecast horizon is 15 min. In order to demonstrate the developed approaches, three-phase reactive and active aggregate power measurements of a multi-tenant commercial building are employed as a case study. The granularity of the data used is 1 s. In total, 52 device profiles are extracted from the aggregate power data. The disaggregation exhibited a highly accurate reconstruction of the measured power with an energy percentage error of approximately 1 %. The indirect power prediction method developed is then applied to the measured power data and outperforms the two persistence forecasts, as well as an artificial neural network designed for 24h-ahead power predictions working in the power domain. Highlights: We propose a disaggregation and prediction algorithm, without any requirement of labeled data. The unsupervised device profile extraction requires only aggregate consumption data. An application of energy disaggregation with particle swarm optimization is shown. Indirect power prediction is performed, using state data from the disaggregation. The power prediction is benchmarked with persistence predictions and a long-term reference.