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Using neural networks to model long-term dependencies in occupancy behavior

Kleinebrahm, Max; Torriti, Jacopo; McKenna, Russell; Ardone, Armin; Fichtner, Wolf

Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider long-term dependencies over several days in occupant behavior. An accurate mapping of long-term dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supply-side fluctuations of renewable based energy systems. The aim of this study is to bridge the gap between social practice theory, energy related activity modelling and novel machine learning approaches. The weaknesses of existing approaches are addressed by combining time use survey data with mobility data, which provide information about individual mobility behavior over periods of one week. In social practice theory, emphasis is placed on the sequencing and repetition of practices over time. ... mehr

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Volltext §
DOI: 10.5445/IR/1000126271
Veröffentlicht am 17.11.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Forschungsbericht/Preprint
Publikationsmonat/-jahr 11.2020
Sprache Englisch
Identifikator ISSN: 2196-7296
KITopen-ID: 1000126271
Verlag KIT, Karlsruhe
Umfang 35 S.
Serie Working Paper Series in Production and Energy ; 49
Vorab online veröffentlicht am 20.02.2020
Schlagwörter activity modelling, mobility behavior, neural networks, synthetic data
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