Energy Use as a Consequence of Everyday Life

Hellgren, Mattias

doi:10.3384/diss.diva-122253

Cited by 4 publications

(6 citation statements)

References 81 publications

(48 reference statements)

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“…Time diaries are a method developed from the time-geographical perspective, and are commonly used in everyday life studies on energy use with a time-geographical approach (e.g., References [27][28][29]). With this method, it is possible to describe and analyze the relationship between activity, location, and movements of individuals.…”

Section: Time Diaries and Interviewsmentioning

confidence: 99%

Integration of Measurements and Time Diaries as Complementary Measures to Improve Resolution of BES

2019

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Building energy simulation (BES) models rely on a variety of different input data, and the more accurate the input data are, the more accurate the model will be in predicting energy use. The objective of this paper is to show a method for obtaining higher accuracy in building energy simulations of existing buildings by combining time diaries with data from logged measurements, and also to show that more variety is needed in template values of user input data in different kinds of buildings. The case studied in this article is a retirement home in Linköping, Sweden. Results from time diaries and interviews were combined with logged measurements of electricity, temperature, and CO2 levels to create detailed occupant behavior schedules for use in BES models. Two BES models were compared, one with highly detailed schedules of occupancy, electricity use, and airing, and one using standardized input data of occupant behavior. The largest differences between the models could be seen in energy losses due to airing and in household electricity use, where the one with standardized user input data had a higher amount of electricity use and less losses due to airing of 39% and 99%, respectively. Time diaries and interviews, together with logged measurements, can be great tools to detect behavior that affects energy use in buildings. They can also be used to create detailed schedules and behavioral models, and to help develop standardized user input data for more types of buildings. This will help improve the accuracy of BES models so the energy efficiency gap can be reduced.

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Section: Time Diaries and Interviewsmentioning

confidence: 99%

Integration of Measurements and Time Diaries as Complementary Measures to Improve Resolution of BES

2019

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“…Hence, to understand how to approach the household use of energy and water, it is important to understand the complicated interaction between individual everyday life activities, socio-cultural norms, social negotiations, gender roles, economic constraints, technological systems and buildings, etc. Commonly, research aiming to investigate and understand the household use of energy and water resources relies on few data collecting methods and often presents aggregated, estimated, modelled or simulated individual behaviour (Widén et al 2009;Willis et al 2011;Hellgren 2015). Even though no modelled or simulated person exists, such studies are useful in the sense that they provide information on possible 'critical' activities or attitudes in relation to for instance the use of energy and water, and what time of day usage can be expected to peak.…”

Section: Everyday-life Studies In Homes -Theoretical Frameworkmentioning

confidence: 99%

“…A time-geographical approach has been used in a range of research studies on individual activities related to, for instance, energy use (e.g. Ellegård & Palm 2011;Hellgren 2015;Isaksson & Ellegård 2015), water use (Krantz 2005), working life (Ellegård et al 1994;Trygg 2014;Trygg & Hermelin 2017), rehabilitation (Andersson 2009;Åström 2009;Orban et al 2012;Bendixen & Ellegård 2014;Örmon et al 2015), mobility Vilhelmson & Thulin 2008;Chen et al 2011) and gender differences (Kwan 1999(Kwan , 2000Scholten et al 2012). Commonly, studies inspired by the time-geographical approach use the time-diary method to collect empirical material.…”

Section: Introductionmentioning

confidence: 99%

“…These studies do not consider the contextual relationship between activities and the social and physical time-space orders, known as the action space. For an extended review of such time-use studies, see Hellgren (2015). The time-diary method proposed here is more open-ended, which increases the likelihood that minor and uncontemplated activities such as routines will also be noted (Ellegård & Nordell 1997).…”

Section: Introductionmentioning

confidence: 99%

“…There are several studies on household energy use using time-diary data in isolation (Widén & Wäckelgård 2010;Aerts et al 2014;Hellgren 2015), aggregating and modelling behaviour aspects of energy use. In studies with qualitative ambitions, time diaries and interviews are used in combination (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A time-geographical mixed-methods approach: studying the complexities of energy and water use in households

Köhler

Trygg²

2019

Fennia

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The objective of this study is to describe and assess a methodology based on a time geographical approach for studying energy and water use in households. Energy and water resources are often used in routinized activities, and in activities considered as private, normal and ordinary, which makes them difficult to explore in research. In this article, we give an account of a mixed-methods approach using time diaries, metering data, interviews and simple observations, and analyse and discuss its methodological and empirical implications from two Swedish case studies. We conclude that the suggested combination of methods, despite some complications, provides a comprehensive account of household energy and water use to which various theoretical perspectives could apply. Energy and water using activities are defined in terms of time, place, quantity, material and social context, and are related to user perspectives on resource use and usage data. Such knowledge provides important input for information campaigns, technological retrofitting and other systemic changes in striving towards sustainability.

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Human home daily living activities recognition based on a LabVIEW implemented hidden Markov model

Guenounou

Aillerie

Mahrane

et al. 2021

Multimed Tools Appl

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The recognition of human daily living activities within a house represents an efficient tool to model its power consumption and is also a good indicator for monitoring the health status of the inhabitants. The problematic of activities recognition in smart homes has been extensively addressed in several studies. In this paper, we present an original interactive tool developed under LabVIEW environment with a graphical user interface allowing the modeling of the daily living activities, based on a machine learning Hidden Markov Model. After an overview of the advantage for the consideration of this model in current human activities, we examine how the associated scientific problematic can find an interest and a solution by the integration of machine learning tools. Thus, the application based on a Hidden Markov model approach, is presented and evaluated using two sets of experimental data from literature. Comparing with results obtained by other daily living activities recognition methods, we point out the very satisfactory recognition performance of the Hidden Markov Model and the likelihood of our development associated to a user-friendly graphical interface. This work opens the way to applications dedicated to the supervision of human daily living activities and / or to the management of the electrical consumption within a smart home equipped with non-intrusive sensors.

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Energy Use as a Consequence of Everyday Life

Cited by 4 publications

References 81 publications

Integration of Measurements and Time Diaries as Complementary Measures to Improve Resolution of BES

Integration of Measurements and Time Diaries as Complementary Measures to Improve Resolution of BES

A time-geographical mixed-methods approach: studying the complexities of energy and water use in households

Human home daily living activities recognition based on a LabVIEW implemented hidden Markov model

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