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Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings

Brucke, Karoline and Arens, Stefan and Telle, Jan-Simon and Schlüters, Sunke and Hanke, Benedikt and von Maydell, Karsten and Agert, Carsten (2020) Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings. [Other]

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Official URL: https://arxiv.org/abs/2006.12940


This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The developed mathmatical description of the disaggregation problem using a state changes matrix belongs to the group of non-event based methods for energy disaggregation. This work includes the development of an objective function in the power domain and the description of position and velocity of each particle in a high dimensional state space. For the particle swarm optimization, four adaptions have been applied to improve the results of disaggregation, increase the robustness of the optimizer regarding local optima and reduce the computational time. The adaptions are varying movement constants, shaking of particles, framing and an early stopping criterion. In this work we use two unlabelled power datasets with a granularity of 1 s. Therefore, the results are validated in the power domain in which good results regarding multiple error measures like root mean squared error or the percentage energy error can be shown.

Item URL in elib:https://elib.dlr.de/137134/
Document Type:Other
Additional Information:Preprint
Title:Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Brucke, KarolineKaroline.brucke (at) dlr.dehttps://orcid.org/0000-0002-4510-8969
Arens, StefanStefan.Arens (at) dlr.dehttps://orcid.org/0000-0002-9449-2282
Telle, Jan-SimonJan-Simon.Telle (at) dlr.dehttps://orcid.org/0000-0001-6228-6815
Schlüters, Sunkesunke.schlueters (at) dlr.dehttps://orcid.org/0000-0002-2186-812X
Hanke, Benediktbenedikt.hanke (at) dlr.dehttps://orcid.org/0000-0001-7927-0123
von Maydell, KarstenKarsten.Maydell (at) dlr.dehttps://orcid.org/0000-0003-0966-5810
Agert, Carstencarsten.agert (at) dlr.dehttps://orcid.org/0000-0003-4733-5257
Date:23 June 2020
Journal or Publication Title:arXiV.org
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Series Name:Computer Science > Neural and Evolutionary Computing
Keywords:particle swarm optimization, load disaggregation, time series
HGF - Research field:Energy
HGF - Program:Technology, Innovation and Society
HGF - Program Themes:Renewable Energy and Material Resources for Sustainable Futures - Integrating at Different Scales
DLR - Research area:Energy
DLR - Program:E SY - Energy Systems Analysis
DLR - Research theme (Project):E - Energy Systems Technology (old)
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems
Deposited By: Telle, Jan-Simon
Deposited On:03 Dec 2020 11:18
Last Modified:03 Dec 2020 11:18

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