A cross analysis of existing methods for modelling household appliance use

Yamaguchi, Yohei; Yılmaz, Selin; Prakash, Nikhil; Firth, Steven; Shimoda, Yoshiyuki

doi:10.1080/19401493.2018.1497087

Cited by 25 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this, we consider the number of household members significant for considering the extent of routine unpaid work performed by man and woman partners in partner households. Not only the extent of the routine unpaid work but also the utilization of domestic appliances is influenced by the number of household members [84,91].…”

Section: Descriptive Statisticsmentioning

confidence: 99%

(Un)Sustainability of the Time Devoted to Selected Housework—Evidence from Slovakia

Knapková

Považanová

2021

Sustainability

View full text Add to dashboard Cite

Domestic appliances play a vital role in modern households. Appliances help simplify domestic work, but individuals become dependent on them. The present paper aims to contribute to the long-standing dilemma among scholars whether domestic appliances help to shorten the time devoted to household chores or not. The paper focuses on the utilization of various domestic appliances in Slovak households and on the influence of their utilization on the time of men and women in partner households and in the single-person households devoted to routine unpaid work activities. The results of the paper are based on the data from original field research conducted in Slovakia in 2015. A total of 1179 partner households, 182 single-man households and 226 single-woman households were included in the analysis. The jamovi version 1.2 statistical program was used to verify the hypothesis by chi-squared goodness of fit test and nonparametric Mann–Whitney U-test. In partner households and in single-man households, usage of automatic washing machines significantly influences time devoted to preparation and maintenance of the clothes. In single-woman households, usage of dishwasher significantly influences time devoted to food preparation. In partner households, men devote less time to routine unpaid work activities than women, regardless of usage or non-usage of domestic appliances.

show abstract

Section: Descriptive Statisticsmentioning

confidence: 99%

(Un)Sustainability of the Time Devoted to Selected Housework—Evidence from Slovakia

Knapková

Považanová

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Changes between weekday and weekend are triggered by people's activities. Surveys as well as diary data can shed light on different characteristics of residential loads (Sokol et al 2017;Buttitta et al 2016;Yamaguchi et al 2019).…”

Section: Measuring Intrinsic Flexibilitymentioning

confidence: 99%

Household electricity demand, the intrinsic flexibility index and UK wholesale electricity market prices

Torriti

2020

Environ Econ Policy Stud

View full text Add to dashboard Cite

During peak electricity demand periods, prices in wholesale markets can be up to nine times higher than during off-peak periods. This is because if a vast number of users is consuming electricity at the same time, power plants with higher greenhouse gas emissions and higher system costs are typically activated. In the UK, the residential sector is responsible for about one third of overall electricity demand and up to 60% of peak demand. This paper presents an analysis of the 2014–2015 Office for National Statistics National Time Use Survey with a view to derive an intrinsic flexibility index based on timing of residential electricity demand. It analyses how the intrinsic flexibility varies compared with wholesale electricity market prices. Findings show that spot prices and intrinsic flexibility to shift activities vary harmoniously throughout the day. Reflections are also drawn on the application of this research to work on demand side flexibility.

show abstract

“…Time use data (TUD) are used as a data basis, which provide information on the temporal course of occupant activities over single days and are available for various countries in the form of population representative samples (Eurostat 2000). Based on occupant behavior, different approaches were developed that connect occupant activities with electrical household appliances and thus generate synthetic electricity demand profiles (Yamaguchi et al 2018). The aim of these studies is to gain a deeper understanding of household electricity demand in order to e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Kleinebrahm

Torriti

McKenna

et al. 2021

Energy and Buildings

View full text Add to dashboard Cite

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 longterm dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supplyside 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. This suggests that practices have a memory. Transformer models based on the attention mechanism and Long short-term memory (LSTM) based neural networks define the state of the art in the field of natural language processing (NLP) and are for the first time introduced in this paper for the generation of weekly activity profiles. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures long-term dependencies in mobility behavior. In a second step, an imputation model enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for multiple use cases one of which is modelling consistent electricity, heat and mobility demand profiles of households. The approach developed provides the basis for making high-quality weekly activity data available to the general public without having to carry out complex application procedures.

show abstract

A cross analysis of existing methods for modelling household appliance use

Cited by 25 publications

References 42 publications

(Un)Sustainability of the Time Devoted to Selected Housework—Evidence from Slovakia

(Un)Sustainability of the Time Devoted to Selected Housework—Evidence from Slovakia

Household electricity demand, the intrinsic flexibility index and UK wholesale electricity market prices

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

Contact Info

Product

Resources

About