How do household activities drive electricity demand? Applying activity-based modelling in the context of the United Kingdom

Pawlak, Jacek; Faghih-Imani, Ahmadreza; Sivakumar, Aruna

doi:10.1016/j.erss.2021.102318

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…In the coming period, the supply from utilities and third parties will increase the government set standards for accessing customer data that can be used to develop new energy management products at home. Home automation suffers from platform fragmentation and a lack of technical standards, a situation where a variety of home automation devices in terms of hardware variations and differences in the software are running on them making the task of developing applications that work in different and inconsistent technology ecosystems more difficult [84,85]. Because of the nature of these devices, there are security, data security, and privacy issues, and patches for bugs in the core operating system may not reach users of older or cheaper devices.…”

Section: Impacts Of Digital Technologies On Energy Consumptionmentioning

confidence: 99%

Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers

2022

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Our paper tackles the topic of the digital technologies (represented by the information and communication technologies (ICT)) and the sustainable transformation and energy safety in households represented by the smart homes and the traditional households using smart appliances, gadgets, as well as smart meters connected to the Internet. The rapid growth and adoption of information and communication technologies, such as computers, mobile devices, sensors, and networks, can seriously affect the ways and amounts of energy used by society on its path toward sustainable development. There is a growing interest in characterizing the net energy impact of ICT as well as the Internet of Things (IoT) that arises from indirect effects that offset (or amplify) the energy directly consumed by ICT equipment. However, the society might need some time to build the trust in these new digital technologies and develop the wide social acceptance for their usage. In order to test these assumptions, we employ an empirical econometric model based on the data obtained from the online survey carried out with the randomly selected respondents (N = 523) from Russian Federation and the four European Union (EU) countries (Czech Republic, Germany, Poland, and Slovakia). Even with regard to the small size of the sample and thence the limitations stemming from that fact, our results demonstrate that familiarity with smart home technologies and the possibility to alter the tariffs and use flexible energy demand increase the safety of residents and consumers. In addition, it appears that energy efficiency is being rewarded, as energy consumption is sensitive to time constraints on supply. Furthermore, we find that respondents’ age (43% of the respondents belonged to the 36–55 age group), positive attitude to smart home technologies and the possibility to exploit the flexibility of demand offered by the variety of energy tariffs during and off-peaks all yield quite strong and positive relationships and correlations with the willingness to accept the smart home technology and the flexibility of demand. At the same time, concerns over personal security or sharing personal data related to energy consumption do not constitute major concerns for the respondents. Our results have some important impacts on the overall strategy for pursuing sustainable economic development and following the Sustainable Development Goals (SDGs) that might be useful for stakeholders and policymakers as well as for the researchers in this field.

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Section: Impacts Of Digital Technologies On Energy Consumptionmentioning

confidence: 99%

Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers

2022

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“…In addition to the influence of dwelling and/or occupant characteristics on energy demand profiles (described in Section 2.6), it is desirable to have an understanding of the occupant activities that drive particular demand profiles-particularly the peaksif demand side management is to be achieved [68,89]. To characterise the relationship between occupant activity and residential energy demand, research has been carried out into linking activity to energy demand with the objective of identifying the key activities that drive energy consumption and help identify where demand reduction or response can be implemented [68,82,[90][91][92].…”

Section: Linking Time Use With Energy Demand Profilesmentioning

confidence: 99%

The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets

Thomson,

Jenkins

2023

Energies

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The availability of empirical energy data from Advanced Metering Infrastructure (AMI)—which includes household smart meters—has enabled residential energy demand to be characterised in different forms. This paper first presents a literature review of applications of measured electricity, gas, and heat consumption data at a range of temporal resolutions, which have been used to characterise and develop an understanding of residential energy demand. User groups, sectors, and policy areas that can benefit from the research are identified. Multiple residential energy demand datasets have been collected in the UK that enable this characterisation. This paper has identified twenty-three UK datasets that are accessible for use by researchers, either through open access or defined processes, and presents them in an inventory containing details about the energy data type, temporal and spatial resolution, and presence of contextual physical and socio-demographic information. Thirteen applications of data relating to characterising residential energy demand have been outlined in the literature review, and the suitability of each of the twenty-three datasets was mapped to the thirteen applications. It is found that many datasets contain complementary contextual data that broaden their usefulness and that multiple datasets are suitable for several applications beyond their original project objectives, adding value to the original data collection.

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“…Three, the proposed model can produce forecasts of activity time allocations that combined with scheduling frameworks (e.g. [42][43][44]) and activitybased energy demand models [45][46][47] provide more accurate and dynamic representation of residential energy consumption.…”

Section: The Importance Of Understanding and Modelling Virtual Activi...mentioning

confidence: 99%

“…First, the activity types were simplified from an initial 276 types in the raw UKTUS data to nine activity categories: work, study, home care, personal care, shopping, leisure, sleep, travel and unknown. For example, home care activity consists of activities indicated by code 3 in UKTUS, named as Household and Family Care, and some other relevant activities reported in code 42, named as Informal Help to Other Households in UKTUS (for the detail of the mapping of the activity types, see [45]). This activity re-categorisation broadly mirrors the trip purpose categories used in travel demand models.…”

Section: Data Processingmentioning

confidence: 99%

Do in-home and virtual activities impact out-of-home activity participation? Investigating end-user activity behaviour and time use for residential energy applications

Rovira

Faghih-Imani

Sivakumar

et al. 2022

Energy and Buildings

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How do household activities drive electricity demand? Applying activity-based modelling in the context of the United Kingdom

Cited by 8 publications

References 43 publications

Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers

Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers

The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets

Do in-home and virtual activities impact out-of-home activity participation? Investigating end-user activity behaviour and time use for residential energy applications

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