An empirical analysis of domestic electricity load profiles: Who consumes how much and when?

Trotta, Gianluca

doi:10.1016/j.apenergy.2020.115399

Cited by 55 publications

(25 citation statements)

References 57 publications

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“…The national roll‐out of smart metres completed in 2014 (Heiskanen & Matschoss, 2016), enabled the provision of more sophisticated pricing structures to households, including real‐time pricing (Ruokamo, Kopsakangas‐Savolainen, Meriläinen, & Svento, 2019). Nevertheless, there seems to exist a trade‐off between the complexity of the tariff model and the engagement of households in demand response programmes (Grünewald, McKenna, & Thomson, 2015; Trotta, 2020b); for instance, only 9% of households had a dynamic electricity price supply contract in Finland in 2017 (Energy Authority, 2018). In the context of the evolution of the electricity market and the strengthening of the role of consumers, higher levels of awareness about electricity use, consumption and prices could, in principle, enhance the attractiveness of complex tariff structures and increase the potential for demand‐side flexibility (Alberini, Khymych, & Šcasný, 2019; Hall, Jeanneret, & Rai, 2016; Prest, 2020).…”

Section: Introductionmentioning

confidence: 99%

Electricity awareness and consumer demand for information

Trotta

2020

Int J Consumer Studies

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With the envisioned growth in the residential electricity demand and increased share of intermittent renewables in the supply mix, consumers will need to be better informed about their electricity consumption and to play an active role in managing their electricity use. However, consumer inattention and lack of information are ubiquitous, especially in household energy‐related settings. Thus, using a novel survey and actual monthly electricity consumption data, this study set out to measure the level of awareness about electricity bills, prices and costs among some Finnish households—as captured by the answers to six questions—and to investigate whether higher levels of “electricity awareness” are associated with electricity savings. In addition, this study analyses the willingness to receive extra information about energy consumption and savings and how it differs between “electricity aware” and “electricity unaware” respondents. The results indicate low levels of “electricity awareness” among the respondents of the survey. Compared to the respondents with little knowledge about electricity bills, prices and costs, the respondents with higher levels of “electricity awareness” tend to consume less electricity. Higher levels of awareness about electricity use and consumption might “materialize” inconspicuous consumption patterns, as opposed to more general facts about the largely invisible environmental consequences of everyday practices. More than two‐thirds of the total number of respondents would like to receive additional information about energy consumption and how to save energy. However, there exists a significant portion of “electricity unaware” respondents who are not only unwilling to receive such information, but are also unaware of their own knowledge deficits. To maximize the impact of any information strategy, decision makers should attempt to engage with this type of consumer; by becoming more aware of their knowledge deficits, people might become more receptive to information that can benefit them.

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Section: Introductionmentioning

confidence: 99%

Electricity awareness and consumer demand for information

Trotta

2020

Int J Consumer Studies

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“…The results emphasize that customer segmentation based on smart meter data and household characteristics is not a sufficient condition for supporting utilities in the design of tailored time-varying rates, which include considering the different needs and resources of consumers and the energy system perspectives (integration of high shares of intermittent renewable generation into grids). Other factors related to variations in socio-material settings [55,62], that result in variations in the specific performance of everyday mundane practices are crucial in shaping electricity consumption profiles and should inform strategies aimed at reducing the peak and overall demand [5,15,19,21]. Thus, when designing a tariff structure, one should be aware of how specific (vulnerable) groups have certain common patterns of consumption, while recognizing that huge variations may also exist within these groups.…”

Section: Discussionmentioning

confidence: 99%

“…A smaller J results in within-group data that are more similar. Prior to K-means clustering, minimum-maximum normalization is scaled to fit in the range of (0,1) to eliminate redundant data and ensure good quality clusters [15,52,53]. The K-means clustering was calculated using the average hourly electricity consumption of the days of the week for each month (135,072 observations).…”

Section: Methodsmentioning

confidence: 99%

“…Other studies go even further in the customer segmentation analysis by combining smart meter data with data containing information about household characteristics. First, clustering techniques are typically used to identify different groups of electricity load patterns, and subsequently, binary or multinomial logistic or probit regressions are employed to infer household characteristics belonging to a particular profile class [8,9,14,15]. While providing valuable information about the probability that a household with specific characteristics belongs to a specific electricity load profile (in terms of magnitude and time of use), these studies fail to account for the heterogeneity of electricity consumption patterns among households with similar characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneity of Electricity Consumption Patterns in Vulnerable Households

2020

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A key aspect of the design of specific tariff structures is to identify and characterize homogeneous electricity consumption profiles. Recent research in residential electricity demand has explored load profile segmentation via cluster analysis combined with descriptive data from the dwelling and occupants, which has partly explained electricity load patterns and their underlying drivers but has failed to investigate any consumption heterogeneity among similar households. Thus, the aim of this paper is to reverse this approach and investigate the extent that households with similar characteristics have different electricity consumption patterns. This study combines population-based register data with hourly electricity consumption data for a sample of 67 Danish households. First, a homogenous household group is selected based on several indicators that signal vulnerability. The specific group under investigation is single-person, older, low-income households in detached housing. Second, K-means clustering is used to identify similarities and differences in consumption patterns. The results indicate four distinct vulnerable household profiles characterized by different start and end times of peak and off-peak times, peak intensities, and overall consumption, which vary across seasons. These profiles are discussed concerning the performance of everyday practices and the design of demand-side management strategies targeted at vulnerable households.

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“…To increase the potential for demand side flexibility, a deeper understanding of domestic electricity load profiles is needed [8], [9]. As results of [10] show, the household electricity sector is changing with increasing integration of PV, smart meters, two-way communication between the customer and the utility, dynamic pricing, and can effectively shift the residential peak away from the time of overall electricity system peak load.…”

Section: Introductionmentioning

confidence: 99%

Potential of End-User Electricity Peak Load Shift in Latvia

Ļebedeva

Borodiņecs

Krumins³

et al. 2021

Latvian Journal of Physics and Technical Sciences

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One of the main challenges in the world as well as in Latvia is the reduction of the fossil fuel consumption. Electricity generation, which is still mainly produced by fossil fuel or nuclear power, is one of the largest shares. Nowadays end-users can efficiently control their electricity consumption by shifting electrical loads and taking into account fluctuating electricity price at Nord Pool. The smart metering systems have been expanding, but for the time being, end-users do not use this possibility efficiently. This study provides an analysis of Latvian electricity consumption specifics and evaluates a potential to shift electricity peak loads. The study includes households, industry and commercial/public sector. Based on the questionnaire distributed among end-users, the initial data on equipment and usage profile were collected. The most essential equipment was reviewed to estimate the potential for load shift. The obtained results show that that there is a positive effect of the load shift. In the household sector, the average electrical load shift can be 6 % (95 GWh/ year) during a period at a cheaper price; in the industry sector on average 37 % (36.58 GWh/ year) and in the commercial/public sector there is no potential due to its specifics (working hours, constant use of refrigerators, etc.).

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An empirical analysis of domestic electricity load profiles: Who consumes how much and when?

Cited by 55 publications

References 57 publications

Electricity awareness and consumer demand for information

Electricity awareness and consumer demand for information

Heterogeneity of Electricity Consumption Patterns in Vulnerable Households

Potential of End-User Electricity Peak Load Shift in Latvia

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