
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12982-9
10.1016/j.heliyon.2024.e36951
e36951
Research Article
Who engages in electricity conservation and to what effect after real-world, high-resolution feedback? An empirical analysis of Korean households with smart meters
Kim Hana hanakim0729@kaist.ac.kr
a1⁎
Caesary Desy b1
Jang Jeongwoo a
Mah Daphne Ngar-yin c
a School of Digital Humanities and Computational Social Sciences, Korea Advanced Institute of Science and Technology, Republic of Korea
b Center for Digital Humanities and Computational Social Sciences, Korea Advanced Institute of Science and Technology, Republic of Korea
c Department of Geography, Hong Kong Baptist University, Hong Kong
⁎ Corresponding author. hanakim0729@kaist.ac.kr
1 Equally contributed as first authors.

28 8 2024
15 9 2024
28 8 2024
10 17 e3695114 11 2023
23 8 2024
26 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Engagement with households to fully realize the potential of demand-side solutions has attracted policy attention. The potential of feedback has been understudied, especially regarding who engages more in electricity conservation. Furthermore, most studies have been limited to the Western context, with only a few that explore Asia. This study fills these gaps by investigating changes in household hourly electricity consumption patterns after its members receive high-resolution feedback. After data balancing, we partitioned 63 households into distinct groups using K-means clustering and investigated consumption changes after the provision of high-resolution electricity feedback through a mobile application. The results indicate mixed effectiveness of feedback: some households reduced consumption by about 13 %, while others increased it between 7 % and 20 %. In addition, statistical analysis using survey responses revealed that households with greater awareness of electricity costs and a stronger interest in climate change were more receptive to feedback. Demographic and housing attributes such as age, building type, and floor count also influenced the feedback effect. The findings recommend enhancing awareness of electricity costs and climate change and developing a better understanding of individuals’ challenges with changing conservation behaviors based on their demographic and housing characteristics.

Keywords

Smart meter
High-resolution feedback
K-means clustering
Electricity conservation
Household characteristics
==== Body
pmc1 Introduction

Policies to realize carbon-neutral targets tend to focus on supply-side energy technology solutions while overlooking demand-side solutions. Supply-side solutions have proved insufficient, which has directed more policy attention to the important yet largely untapped potential for demand-side management to contribute to energy transitions [1]. Engaging households in demand-side management has been documented as a major challenge despite the existence of many such policies around the world. Households often struggle to capture the cost and electricity consumption implications because of the “abstract, invisible, and untouchable” nature of electricity [2].

Smart meter-based energy consumption feedback has been increasingly recognized as an important means for engaging households in energy conservation. Smart meters (SMs) are advanced meters utilized to monitor consumption of resources, including not only electricity but also water, heating, and gas. Conventionally, electricity feedback has been delivered in a delayed manner through monthly electricity bills. Nowadays, SMs deliver electricity consumption in more granular time intervals such 15 min and provide diverse information such as historical electricity consumption covering previous months, present electricity consumption, and the neighborhood's average electricity consumption. Customers can access this high-resolution diverse information through in-home displays (IHDs) or mobile applications [2,3]. applications. This high-resolution feedback has created new opportunities to enhance households' awareness of the implications of their electricity-consuming activities and subsequently promote energy conservation behavioral change [[2], [3], [4], [5]].

The effectiveness of high-resolution feedback enabled by smart meters in promoting energy saving in households has been extensively studied [[6], [7], [8], [9]]. Empirical studies have found that the impact of SM feedback ranges from an increase in electricity consumption of 5 % to a decrease of up to 20 % [[10], [11], [12], [13]]. Providing feedback through various types of feedback, such as printed monthly electricity bills, IHDs, smartphone apps, or website platforms, can positively impact electricity conservation [14]. More frequent feedback is expected to lead to changes in energy consumption by providing households with higher resolution data, which enhance households’ awareness of their electricity consumption [[2], [3], [4]] and stimulate behavioral changes [5]. In addition to promoting electricity conservation, feedback has been shown to be effective at reducing peak load, for example by 5 % in Schleich et al. [15].

Feedback has, however, mixed outcomes. In some cases, electricity feedback has not produced a statistically significant effect on electricity conservation [16]. This could be attributed to the impact of various factors, including “frequency, content, breakdown, presentation, inclusion of comparisons, and combination with additional information and other instruments” [2]. While experimental conditions can control these factors and provide refined information to each group, in the real world, web- or mobile-based feedback provides simultaneous content. This calls for empirical studies that investigate, measure, and evaluate the effectiveness of feedback at affecting household energy conservation behavior in real-life settings.

While feedback itself matters, its effectiveness depends on consumer engagement [3,17]. Henn et al. [18] emphasized that “effectively saving energy depends not only on a person's engagement in such an opportunity per se—i.e., registering for a web portal—but also on how rigorously the person makes use of this energy-saving opportunity.” Signing up for a smart meter service is just the first step in consumer engagement. Individuals need to take time to access the platform, check their electricity consumption information, and conduct electricity-saving activities based on the feedback. Therefore, even though the same feedback is provided, impacts on energy behavioral change may vary across households [[19], [20], [21]]. It is therefore important to investigate who engages in electricity conservation and what factors influence that engagement.

Real-time feedback implementation based on SMs has been supported in many countries—for example, EU member states, according to EU Electricity Directive 2009/72/EC [22], and the U.S., according to Section 1252 of the Energy Policy Act of 2005 [23]. The potential of SMs to reduce suppliers' operational costs and consumers’ electricity bills has been widely recognized [17].

Previous studies in this field focused on Western economies and were at trial or pilot scales. Empirical analysis has been limited, particularly in non-Western contexts. This study therefore focused on Korean households, with an aim of investigating the effect of real-world, high-resolution feedback on electricity saving and who engages in electricity conservation. South Korea is a significant case of SM-enabled energy transitions. The country has invested in installing smart meters in every household by 2024 [24]. As of 2019, the deployment rate of SMs in South Korea reached 44 %, substantially higher than the world average (14 %), although Italy, Sweden, Finland, and New Zealand have installed SMs in every household [25]. Our empirical study of Korean households makes an empirical contribution to the literature on smart grid-enabled behavioral change and informs policy making.

This empirical study involved 100 Korean households. It aimed to answer the following research questions: (1) Whether and to what extent did households change their electricity consumption level when they received high-resolution feedback? (2) Were any specific households more engaged after receiving high-resolution feedback? (3) Which demographic, socioeconomic, or attitudinal factors influence changes in household behavioral patterns?

We collected hourly electricity consumption data from smart meters installed in participating households and used K-means clustering (n = 63) to analyze changes in electricity consumption patterns after receiving feedback. We also conducted statistical analysis of online survey data to identify which demographic, socioeconomic, and attitudinal factors explain differences in energy consumption in response to high-resolution feedback.

This paper is structured as follows. Section 2 provides a literature review on SM-enabled high-resolution feedback and household engagement in energy conservation. Section 3 provides the context of household engagement in South Korea. Section 4 presents the methods used. Section 5 presents and discusses the findings of the case study, followed by a conclusion and policy implications in Section 6.

2 Literature review

The effects of feedback on electricity consumption conservation have been well documented. Savings are generally achieved [26,27]. However, some studies have not identified a meaningful effect—for example, Du et al. [20], Hargreaves et al. [28], and Nilsson et al. [29]. These results could be attributed to research design, feedback devices, and household characteristics [21]. This section reviews not only feedback itself but also the people who can affect its impact.

2.1 The impact of how feedback is delivered

Feedback features influence how information is delivered and presented to users. Thus, those features can affect consumers' perception of the implications of the information. For example, Li and Cao [30] found that real-time feedback did not lead to noticeable electricity conservation. They attributed that result to low awareness of the relatively high-tech devices that delivered the feedback. This implies that feedback features—how information is delivered—matter. Krishnamurti et al. [31] summarized the features of IHDs into five categories, which are valid for electricity feedback in general: (a) units, such as power (kW), energy (kWh), total cost ($), cost for a certain period, or CO2 emissions; (b) physical range, such as by room, household member, or specific appliance; (c) time, such as monthly, daily, or hourly electricity use; (d) format, which could be a chart, table, numbers, text, or combination of audio and visual; and (e) a target or comparable entities, such as one's own historical consumption or other people.

Physically segregated feedback captures the electricity consumption or cost implications of engaging in a specific activity or using a certain electric appliance. Direct feedback about showering was found to be effective even though participants (hotel guests) would not enjoy the reduced bills [32]. A summary of the aggregate electricity consumption of appliances teaches recipients more about electricity consumption, but household participants have a strong preference for detailed, appliance-specific feedback [31].

Time segregation is similar to physical segregation in that it helps individuals directly capture the electricity consumption implications of their activity. Even if it is not physically segregated, individuals can infer the cost or implications of their activity. For instance, if they use an electric oven for 1 h starting at 7 p.m., individuals can see the increment in their hourly electricity consumption. Fischer's review [2] recommended more time-segregated feedback, but time segregation has mixed implications [13,33]. Hourly electricity consumption and cost information could fail to drive statistically significant changes because hourly costs are trivial. This confirms findings that small, marginal monetary compensation does not promote efforts and could even have a detrimental effect [34].

The format of feedback can promote individuals’ understanding of the implications of their daily activities. For example, a curve portraying how much energy is required for various daily activities enables a household to comprehend its own activity pattern and identify reduction opportunities [35].

The effectiveness of having comparable entities or targets has been supported in a great deal of literature. For example, De Dominicis et al. [36] and Schultz et al. [37] found household electricity consumption information is effective at reducing electricity consumption when it is provided in a way that compares it with similar households’ electricity usage (normative feedback). In general, the feedback effect is maintained for only a short time and then diminishes [10,21], but normative feedback contributes to the long-term durability of behavioral changes, even in dormitories where occupants do not pay their own utility bills [19].

A different framing approach can be added to Krishnamurti et al.'s [31] comparable entities or targets. The way information is framed is significant: Bager and Mundaca [38] found that electricity consumption declined more when information was framed as a loss, as “money lost from electricity consumption.”

Furthermore, feedback devices can have an influence. Direct feedback (e.g., through real-time IHDs) is believed to be more effective at making energy use visible than indirect feedback (e.g., through bills) [39]. Like IHDs, TV-channel feedback was favored over SMs for providing energy consumption to participants and as a useful way to learn about their appliances' consumption [40]. Despite the promise of web- and app-based feedback strategies for promoting energy efficiency and conservation [14], it is crucial to consider two important factors. First, a web or smartphone application (except an IHD) can easily become overlooked and hidden from residents’ view [41,42]. Second, the information is mainly accessible only to the person who installed the app, meaning not all family members can access it [41]. Thus, the engagement level could differ among family members, which could limit the effect.

In practice, real-time electricity consumption data is delivered not in the refined manners tested in studies but in a complicated manner that conveys information in diverse forms (e.g., hourly and monthly cost, consumption amount in kWh, expected incentives according to savings, and comparison with neighbors). This diverse, complex information could challenge customers’ comprehension of its implications and how should they act in response [3]. Therefore, the real-life effect could be different from research findings.

2.2 The impact of who receives the feedback

The literature has investigated the effects of the content, type, and frequency of feedback. Even when consumers have been the focus, only a specific, homogenous segment of them has been studied. For example, Nilsson et al. [43] focused on highly educated households with high incomes in an investigation of the potential of home energy management systems. These approaches set aside household characteristics as control variables and focused on identifying the intervention effect [44,45].

Household characteristics have been assessed not for SM engagement (use of SMs) but for SM acceptance (agreeing to install or purchase SMs). Bugden and Stedman [46] used a survey to investigate the effect of household characteristics such as sociodemographic features, climate risk perceptions, and familiarity with the technology on the adoption of and engagement with SMs. They found that price consciousness and familiarity with SMs were positively related to SM acceptance. They also found that income had a great impact on intention to use SMs and that older individuals were less likely to use the technology. While a plethora of studies have looked into household characteristics, including demographic features and dwelling types, these used surveys to focus on SM acceptance [[47], [48], [49], [50]].

Household characteristics have been used to understand and predict household electricity consumption patterns using big data generated from SMs. For example, Tang et al. [51] investigated the relationship between household socioeconomic attributes and electricity consumption patterns by analyzing 433 households' electricity consumption over 3 years through machine learning techniques. They found that age and educational level had a strong influence on load patterns. Education levels were relevant to both weekday and weekend electricity consumption data, but age impacted weekdays in particular because elderly residents' flexibility in electricity consumption greatly affected the load patterns. Guo et al. [52] classified 2874 Irish households into five groups according to their daily load pattern and considered household characteristics such as occupation and the number of residents, appliances, and bedrooms. The group with the largest electricity consumption was affluent families with large numbers of residents, bedrooms, and appliances. Households with the lowest consumption had the opposite characteristics: middle-income, single-adult households with relatively few members living in small houses. The households’ sociodemographic features were used to explain the difference between perceived and actual electricity consumption [53].

These studies linked household characteristics with consumption patterns but did not explore the relationship between those characteristics and actual changes in electricity consumption patterns after high-resolution feedback was given. Several studies have looked into household characteristics and the effect of providing more frequent electricity consumption information (see Table 1). Schleich et al. [15] analyzed the relationship between electricity consumption and household features—including income, education, household size, dwelling size, working status, and number of major appliances—but they did not scrutinize how different households responded to feedback from an SM. Normative feedback is known to have a larger, sustained effect on energy conservation. De Dominicis et al. [36] advanced the understanding of normative feedback by focusing on households’ identification. When people have an identity similar to the reference household, the long-term effect of normative feedback using IHD is stronger. Also, when people have a high level of subjective norms, the perceived social pressure related to doing or not doing a given behavior, the electricity conservation effect from normative feedback is much larger [19]. Similar findings indicate that people with higher consumption than average change their use of air conditioning to follow the social norm [54].Table 1 Studies relating household characteristics and feedback effects.

Table 1Study	Household characteristics	Feedback specifications	Findings	
Schleich et al. [15]	Income, education, household size, dwelling size, working status, number of major appliances	• Web-portal feedback (with 1-day delay at most) and electricity conserving tips

• Monthly printed feedback (daily, weekly, and monthly household electricity consumption) and tips

	• Positively associated with electricity consumption: education, household size, dwelling size, number of major appliances

• Negatively associated: education, working status

	
De Dominicis et al. [36]	Level of identification with the reference household in normative feedback	Three types of feedback using IHD:• normative

• consumption (kW)

• consumption and cost

	• Households with high level of identification with the reference group had greater reductions

	
Anderson et al. [19]	Level of subjective norms	Two types of weekly feedback:• consumption in kWh and tips

• normative feedback and tips

	• Individuals with high level of norms had greater reduction in response to normative feedback

• Individuals with low level of norms increased electricity consumption even with feedback

	
Wang et al. [16]	Job status, gender, responsibility for and frequency of bill payment, age, education, income, family size, children, dwelling and living room size, air conditioning	Three types of feedback:• current

• current and historical

• current, historical, and normative (ranking)

	• Feedback led to greater reductions in households with small family size and without children.

	
Henn et al. [18]	Environmental attitudes	High resolution feedback through web portal	• High resolution feedback led to electricity saving only when people had a sufficient level of pro-environmental attitude

	
Puntiroli & Bezençon [55]	Biospheric attitudes, feedback duration	No specific information about feedback	• People with low biospheric value failed to reduce electricity expenditures

• When feedback is provided in the long term (more than 3 years), highly biospheric people reduce their electricity consumption

	

In public housing, the different electricity consumption patterns of individual households lead to large differences in electricity savings, including slightly negative or zero savings. Wang et al. [16] found family size and children are significantly correlated with the feedback effect, regardless of the feedback type. Households with few family members and no children are likely to conserve the most electricity when feedback is given.

Some recent environmental psychology literature has looked into the role of environmental attitudes in relation to electricity saving when smart feedback is given [18,55]. Henn et al. [18] and Puntiroli and Bezençon [55] found that high-resolution feedback is effective only when people have a certain environmental attitude. In addition, Puntiroli and Bezençon [55] found that the duration of feedback is only effective at stimulating individuals to reduce electricity expenditures when they have a high pro-environmental attitude. A pro-environmental incentive, like the utility company planting a tree if the household reaches a 5 % reduction goal, is insufficient for reducing electricity consumption by itself [56]. The authors pointed out that enhancement of people's environmental awareness would promote behavior changes.

3 Case context

This study provides an empirical analysis of the effect of SM-enabled high-resolution feedback on the electricity conservation behavior of Korean households. South Korea is trying to shift from a centralized electricity system to a more distributed one. According to its 10th Electricity Demand and Supply Basic Plan [57], South Korea aims to expand the share of distributed energy sources to 23.3 % by 2036. The variability of renewable energy is a substantial threat to the reliability of the system. In 2022, solar PV and wind power generation was curtailed 132 times in Jeju, resulting in the loss of 28.9 GWh. Accordingly, deploying a smart grid and relevant infrastructure such as smart meters is imperative, as is people's engagement with this infrastructure [24].

SMs, also called advanced metering infrastructure, can measure or store data at certain intervals and enable two-way communication between suppliers and consumers [58]. Smart meter deployment has made great progress in South Korea due to the absence of any obstacle to public acceptance of smart grid deployment [59,60]. This positive reception can be attributed to the fact that households do not incur any financial burden—the installation of smart meters is perceived as merely a substitution for outdated meters. By the end of 2022, SMs had been installed in 11.7 million residential premises. The South Korean government has set a target of installing SMs in 22.5 million households by 2024 ([24]; see Table 2). The main targets include detached houses, townhouses, and low-rise apartments (see Fig. 1a and 1b). The residential electricity billing system is applied differently in these housing types. Korea Electric Power Corporation (KEPCO), the state-owned monopoly on national transmission and distribution in South Korea, distributes electricity at high voltage to high-rise apartment complexes.Table 2 Smart meter deployment in Korea (2022).

Table 2Ownership of meters	Status (2022)	Target	
KEPCO (22.5 million households)	Installed in 11.7 million households (52 %)	100 % installation by 2024	
Apartment complex with remote metering (3 million households)	Install communication infrastructure in 10 thousand households (2022–2023)	Consideration of scaling up after evaluation of the project	
Apartment complex with manual metering (8 million households)	Financially support the transition to smart meters (1.2 million households)	23 % by 2023	
Source [24].

Fig. 1 a) Smart meters installed in detached houses in South Korea, and b) zoom-in image of the installed smart meter (image courtesy of Hana Kim).

Fig. 1

KEPCO makes separate contracts with low-rise residential houses. It installs, owns and manages the SMs for those houses, and it can collect electricity consumption at 15-min intervals and provide the households with electricity feedback at 1-h intervals. In contrast, as large apartment complexes that consist of many units are aggregated into a single customer, KEPCO does not have access to the electricity consumption of individual apartment units. Rather, private companies manage SM installations for individual units in large apartment complexes and deliver electricity bills to each unit.

KEPCO informs households that they can access high-resolution electricity consumption data, including hourly electricity information collected from SMs, through the free smartphone app Power Planner that it developed and operates (see Fig. 2). However, according to personnel at KEPCO's Department of Smart Metering, the installation rate of the app is low (about 18 %) and actual utilization is even lower (5 % of installed households) despite active promotion campaigns with generous raffles. The effect of this high-resolution feedback data had not yet been evaluated in Korea.Fig. 2 The layout of a real-time electricity information feedback app (Power Planner).

Fig. 2

4 Methodology

4.1 Data collection and balancing

Households in Daejeon, the fifth largest city in South Korea, were recruited from June 14 to July 28, 2021, through advertisements on diverse channels, including Instagram, Facebook, and Danggeun market (a representative person-to-person secondhand online marketplace in South Korea), and posting leaflets on low-rise houses. Even with privacy concerns about the nature of an SM study [38] and the COVID-19 situation, 519 participants voluntarily enrolled in our program. However, only about a quarter of these households were found to be eligible based on the status of their SM installation. The lack of information on which households had smart meters—strict regulations on personal data in Korea prevented us from obtaining this information—complicated the recruitment process and made it difficult to accurately represent the city's demographic profile. Nonetheless, 100 households agreed to provide their electricity consumption data. Voluntary participation is often criticized for its possible selection biases [32], but the participants were not very aware of their electricity consumption and did not even know whether their households had smart meters prior to participation in this study.

After mobilization, we had participants install Power Planner (see Appendix A) on August 15, 2021; hence, Power Planner was the means of electricity consumption feedback to the participants. We did not set aside a control group for this intervention because it was not possible for us to mobilize households to provide hourly electricity consumption information without using high-resolution feedback. The installation of smart meters enabled the collection and provision of granular electricity consumption, and that consumption information could only be obtained with agreements signed by households.

We collected 1-h interval electricity consumption data from 100 households before (September and October 2020) and after (September and October 2021) the installation of Power Planner from KEPCO Energy Marketplace (https://en-ter.co.kr/main.do), a platform for providing diverse electricity data and services. In addition to hourly electricity consumption data, an online survey using Survey Monkey was conducted with the 100 households to understand their demographic features, such as income and household size, and their attitudes toward electricity conservation and climate change (see Table 3). The largest share of participants was in their 30s, had an undergraduate level of education and a monthly income level of about 1500–2,250USD (2000–3000 thousand KRW), and resided in multiplex houses (see Table 4).Table 3 Household characteristics obtained from survey.

Table 3Characteristics	Items	Notes	
Knowledge level about electricity consumption	quizsum	Knowledge about electricity consumption	
Awareness of electricity bills and rates	elcharge_gap	Difference between actual and stated electricity bills	
elcharge_basic_score	Know the basic electricity charge	
Awareness of electricity conservation and climate change	elimportance	Importance of electricity conservation	
elinterest	Interest in electricity conservation	
elrelevance	Relevance of electricity conservation	
elefficacy	Ability to engage in electricity conservation	
elshould	Responsibility for electricity conservation	
climportance	Importance of climate change	
clinterest	Interest in climate change	
clrelevance	Relevance of climate change	
clefficacy	Ability to engage in climate change responses	
clshould	Responsibility for climate change responses	
Willingness with respect to electricity conservation and climate change	elact	Willingness to conserve electricity	
elsearch	Willingness to look for additional information regarding electricity conservation	
eladvice	Willingness to make recommendations to family members and friends regarding electricity conservation	
clact	Willingness to act against climate change	
clsearch	Willingness to look for additional information regarding climate change	
cladvice	Willingness to make recommendations to family members and friends regarding climate change	
Demographic features		Income, age, education, house area, family size, number of children, disabled family member, house type	

Table 4 Description of households’ demographic data.

Table 4Demographic variable	Summary (N = 100)	
Age	
 20s
 30s
 40s
 50s
 60s	28
32
23
9
8	
Monthly income (in USD)	
 <749
 749–1498
 1498–2248
 2248–2997
 2997–3746
 >3746	12
16
31
24
7
10	
Education	
 Less than high school
 High school
 Undergraduate school
 Graduate school	3
28
51
18	
House size	
 <33 m2
 33 m2–66 m2
 66 m2–99 m2
 99 m2–132 m2
 >132 m2	20
33
28
11
8	
House type	
 Single house
 Multiplex house
 Apartment
 Studio apartment
 Others	33
21
43
1
2	

Because of technical issues such as poor network connection with their SMs, some participating households had no record of their electricity consumption data for over a month and others had only a few days of records in a month. After balancing the dataset to deal with these missing data points, hourly electricity consumption data from 63 households for 114 days in September and October of 2020 and 2021 were used for the analysis. This study separately investigated electricity data on weekdays (79 days) and weekends (35 days) to capture different behavioral patterns (see Fig. 3a, 3.b and 3.c). The average daily electricity consumption was 6.62 kWh/day/household, with weekday and weekend averages of 6.51 kWh and 6.85 kWh, respectively (see Table A1). In Korea, overall electricity consumption is primarily driven by household appliances, including refrigerators, air conditioners, and electric stoves. The dominant heating system is gas, and electric vehicle charging is separated from household electricity bills. Weekend and weekday electricity consumption patterns are different [15]; therefore, this study tried to see the impact of access to high-resolution feedback not only on weekdays but also on weekends.Fig. 3 Boxplot diagram for electricity consumption: (a) all time, (b) weekday, (c) weekend.

Fig. 3

4.2 Data analysis

4.2.1 Clustering analysis

This study used the K-means clustering method to classify households based on changes in electricity consumption patterns after high-resolution electricity feedback was given. This unsupervised learning method groups an unlabeled dataset based on similar properties in its structure [61]. K-means is the most popular clustering method because of strengths such as easy implementation, fast operation, and efficient computation time [62]. It has been used in statistics, pattern recognition, and image processing [63,64]. K-means aims to partition N-dimensional feature data into k sets on the sample [65]. See details in Appendix B.

Clustering methods have been widely utilized in analysis of electricity consumption in order to determine user profiles or patterns by grouping households based on similar consumption behavior [[66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76]]. After understanding consumption patterns, additional analysis can be conducted, such as evaluating demand flexibility [68], examining the electricity load curve [72,73,75], and examining the influence of factors on the clustered electricity consumption pattern [67,71]. In this study, the daily electricity consumption values from the households resulted in groups clustered according to similarities in consumption trends: whether the September and October trend from 2020 to 2021 was decreasing, increasing, or unchanging.

4.2.2 Statistical analysis

First, the consumption pattern changes in different clusters were investigated to see whether the change within a cluster after the provision of high-resolution data through the Power Planner was statistically significant. This approach corresponded to the customer base load (CBL) approach—comparing electricity consumption after the intervention with electricity consumption before the intervention—KEPCO uses to estimate the incentive payment for participants in Korea's demand response program.

Next, the clustered households were analyzed based on characteristics such as knowledge of electricity consumption, awareness of electricity bills and rates, awareness of climate change, willingness to conserve electricity, and demographic features. These household characteristics can provide hints to understand different responses to electrical usage feedback through Power Planner. Fig. 4 illustrates the analysis process. The statistical tests began with determining whether the data had a normal distribution, followed by tests that analyzed the significant differences between the clusters (details of the statistical tests are in Appendix C). The statistical analysis was conducted using the SciPy python library [77].Fig. 4 Flowchart of statistical analysis.

Fig. 4

5 Results and discussion

5.1 Mixed impacts of high-resolution feedback on electricity consumption changes

This study used the K-means clustering algorithm to group households into distinct clusters according to changes in their electricity consumption patterns between September and October of 2020 and 2021. The clustering was conducted separately for weekday and weekend data to identify different patterns of behavioral change on weekdays and weekends. We adopted the silhouette method to specify the optimal number of clusters (see Figure B1). The result indicated that grouping the 63 households into two groups was optimal (see details in Appendix B). Cluster WD-0 and Cluster WD-1 are from the clustering of weekday data, and Cluster WE-0 and Cluster WE-1 are from the clustering of weekend data.

Our study found that the impact of high-resolution feedback on households on weekdays varied across the two clusters of households, which implies the feedback had mixed impacts in the participating households. High-resolution feedback positively impacted households in Cluster WD-1 (n = 24) but failed to induce energy conservation in Cluster WD-0 households (n = 39), as shown in Fig. 5a. Instead of reducing electricity consumption, households in Cluster WD-0 increased it. Households were also classified into two groups using weekend data, based on the different impacts of high-resolution feedback (see Fig. 6a). Cluster WE-1 indicates a positive feedback effect, while Cluster WE-0 does not. Similar to the results observed on weekdays, not all households decreased electricity usage on weekends.Fig. 5 Household clustering using weekday dataset: (a) K-means results, (b) boxplot comparison.

Fig. 5

Fig. 6 Household clustering using weekend dataset: (a) K-means results, (b) boxplot comparison.

Fig. 6

Additional statistical analysis (a Wilcoxon signed rank test was conducted because the data did not pass the normality test) showed that average daily energy consumption significantly differed before and after the high-resolution feedback (see Fig. 5b for weekdays and 6.b for weekends). Our study approach closely resembles the CBL-based approach employed by numerous programs in Korea that examine the impact of measurement.

Given different behavioral patterns and responses to the high-resolution feedback between weekdays and weekends—some households would not be likely to reduce electricity consumption on weekends while they were likely to on weekdays, and others the opposite—we merged the results from weekdays and weekends to pinpoint households exhibiting consistent patterns across both time periods and their characteristics, as shown in Fig. 7: 24 households with an increasing electricity consumption trend on both weekdays and weekends (Group WDWE-0), 19 households with a decreasing trend on both weekdays and weekends (Group WDWE-1), and 20 households with a mixed trend (Group WDWE-2).Fig. 7 Boxplot comparison of EC from merged clustering results for weekdays and weekends.

Fig. 7

The positive impacts of high-resolution feedback on energy conservation behavior have been extensively documented [14,15,[78], [79], [80], [81], [82], [83]]. Our findings provide valuable empirical data from a non-Western society to support the claims that smart meters' high-resolution feedback can positively impact households’ energy-saving behavior. This empirical study in South Korea shows that feedback led to an approximate 13 % reduction in daily average consumption on both weekdays and weekends in some households (Cluster WD-1 and Cluster WE-1) and about a 6 % reduction in daily average consumption in the merged results (Group WDWE-1). This finding in a non-Western context is consistent with the literature, which reports electricity consumption changes ranging from an increase of 5 % to a decrease of 20 % [[10], [11], [12], [13]].

Other studies have shown that high-resolution feedback may not lead to energy conservation [9,18,20,21,29,37,55,84,85], and this study adds to the literature by shedding light on the magnitude of this undesirable increase in electricity consumption. Our analysis divided households into clusters, one with desirable changes and the other with increased electricity consumption after receiving high-resolution feedback. Our analysis showed that some households’ daily electricity consumption increased by 7 % on weekdays (Cluster WD-0), 20.4 % on weekends (Cluster WE-0), and about 5 % in the merged results (Group WDWE-0).

5.2 Electricity consumption changes with household characteristics

This study specified factors that could explain why households responded differently by conducting an analysis of statistically significant differences in the survey responses based on household characteristics (see Table 3). Our analysis shows that awareness of electricity costs, interest in climate change issues, age, and housing type are key factors that explain the different effects of high-resolution feedback on household energy-saving behavior.

Cluster WD-1 and Cluster WE-1 households that responded positively to the feedback on weekdays and weekends, respectively, knew their electricity charges better than others (see Fig. 8a and 8b). This finding aligns with some previous studies. People with a greater understanding of the cost implications of their electricity consumption (e.g., electricity rates and the power of the electric appliances they use) are more likely to consume less electricity and appreciate the value of feedback [86]. Individuals who do not monitor their electricity consumption and are insensitive to electricity prices tend to be less willing to change their behavior [5].Fig. 8 Comparison of awareness level of electricity charge between clusters using weekday data: (a) boxplot from weekday results, (b) mosaic plot from weekend results. Note: Red indicates Cluster WD-0 (with increasing electricity consumption) and green indicates Cluster WD-1 (with decreasing electricity consumption). *p < .05, †p < .1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 8

Our analysis shows that Cluster WD-1 and Cluster WE-1 (weekday and weekend data) households with higher daily average electricity usage had a positive response to the feedback, and those with lower electricity usage experienced an increase in consumption on both weekdays and weekends. This suggests households with higher electricity consumption may be more conscious of their energy usage and more receptive to feedback, and households with lower electricity consumption may not see the value of feedback or may not be as concerned about their energy usage. This aligns with previous findings that individuals who consume more electricity may be more motivated to conserve energy because of higher costs [4,54,[87], [88], [89]].

The findings reveal that households with strong concerns about climate change decreased their electricity use either on weekdays (Cluster WD-1 in Fig. 9a to 9 d) or merged periods (Group WDWE-1 in Fig. 10a and 10b). This aligns with previous research findings that environmental attitude is a key factor in electricity conservation because it not only motivates individuals to make environmentally responsible choices but also encourages them to stick to these decisions [18,55,90]. The findings also support the notion that there is a link between an individual's environmental attitude and their energy conservation behavior [18].Fig. 9 Comparison of concerns on climate change between Cluster WD-0 and Cluster WD-1 from weekday results. Note: Red indicates Cluster WD-0 (with increasing electricity consumption), and green indicates Cluster WD-1 (with decreasing electricity consumption). *p < .05, †p < .1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9

Fig. 10 Comparison of concerns on climate change between Group WDWE-0, Group WDWE-1, and Group WDWE-2 from the merged results. Note: Red indicates households with increasing electricity consumption, green indicates households with decreasing electricity consumption, and gray indicates a mixed trend. †p < .1, *p < .05. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 10

Previous studies have demonstrated that a range of household demographic factors, such as age, income, and education, can influence household electricity conservation behavior [78,83,91]. In this study, we tested differences in demographic features between clusters of households. We found that the age of Cluster WD-1 and Group WDWE households was marginally (p < .1) higher than that of Cluster WD-0 and Group WDWE-1 households (see Fig. 11a and 11b). Middle-aged (around 30s and 40s) households were more likely to decrease their electricity use across all data segments (weekday and merged) in response to high-resolution feedback.Fig. 11 Comparison of age level: (a) Cluster WD-0 and Cluster WD-1 from weekday results, (b) Group WDWE-0, Group WDWE-1, and Group WDWE-2 from the merged results. Note: Red indicates households with increasing electricity consumption, green indicates households with decreasing electricity consumption, and gray indicates a mixed trend. †p < .1, *p < .05. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 11

In addition to age, housing type was associated with energy-saving behavior. The weekend and merged data (Cluster WE-0 and Group WDWE-0, respectively) revealed that households with increased electricity consumption tended to reside in row-houses (see Fig. 12, Fig. 13), and those with lower consumption were more likely to live in other dwelling types. This correlation may be caused by the typical row-houses apartment in South Korea being old and less energy efficient than a newer building [82]. Other studies have demonstrated that household variables such as income, floor area, and family size influence electricity consumption [15,39,92]; however, this study did not find significant differences in these demographic factors among the clusters.Fig. 12 Comparison of housing type between Cluster WE-0 and Cluster WE-1 from weekend results. Note: Red indicates clustered households with increasing electricity consumption, and green indicates clustered households with decreasing electricity consumption. *p < .05, †p < .1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 12

Fig. 13 Comparison of housing type between Group WDWE-0, Group WDWE-1, and Group WDWE-2 from the merged results. Note: Red indicates households with increasing electricity consumption, green indicates households with decreasing electricity consumption, and gray indicates households with a mixed trend. †p < .1, *p < .05. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 13

5.3 Policy implications

The findings of this study have policy implications for carbon-neutral and energy policy initiatives, particularly demand response programs. Policymakers can be informed about which subgroup of households tends to adopt ecofriendly behavioral changes in response to electricity feedback. High-consumption households were observed to reduce their consumption after installing the Power Planner application. The installation and use rate of Power Planner is still very low, thus it is necessary to promote it, particularly to high-consumption households.

In contrast, low-consumption households were observed to exhibit a negative response in light of the increase in electricity demand after the provision of feedback [4,54,[87], [88], [89]]. This suggests an additional intervention strategy might be necessary. A utility company or a government can provide an incentive if a household maintains a certain electricity consumption level, as it does in the eco-mileage program, a rebate program for energy conservation in Seoul [93].

In addition, the findings shed light on the importance of awareness and attitudes toward climate change. People often do not recognize the link between their daily electricity consumption and climate change [[94], [95], [96]]. A feedback service can function as a useful campaign tool or platform by developing climate literacy and sharing important electricity conservation tips through messages [13,85]. This enhanced feedback service could raise awareness as well.

Feedback was effective for middle-aged persons who are income earners in households with large families. Use of this mobile application or website-based feedback is exclusive because signing up for it requires personal authentication; thus, it tends to not be shared among family members. Sharing the application and educating other family members, including children, would be a way to influence behavior. For instance, school campaigns that promote feedback mechanisms to encourage energy conservation could lead to significant changes in household habits.

Last but not least, weekday and weekend reduction responses should be accounted for in future electricity demand management: weekday electricity reduction was observed to be greater than weekend reduction. People spend more time at home on weekends, so additional tips for conserving electricity through behavioral changes can be provided, such as using low-temperature water for the washing machine and reducing its run, taking shorter showers, and using LED bulbs.

There are limitations to this study. First, we used a relatively small number of households for analysis (n = 63) after data balancing. A future large-scale field study may identify variables that were not statistically significant in this study. Nevertheless, the findings of this study with a relatively small group of volunteers can be a valuable point of reference for non-Western nations. Second, we did not investigate the mechanism that explains the association between household characteristics and feedback effects, which warrants future study.

6 Conclusions

Our findings in the Korean context shed light on two effects of high-resolution feedback on household electricity savings. First, real-world, complex, high-resolution feedback was effective, but not for every participating household. Through K-means analysis of weekday or weekend hourly electricity consumption data, 63 participating households in Daejeon, South Korea, were clustered into two distinct groups according to their electricity consumption pattern changes. One cluster had average daily electricity consumption reduction between 6 % and 13 %, and the other cluster increased its electricity consumption between 7 % and 20 %. Second, household characteristics including housing type, demographic features such as age, and attitudinal factors such as awareness of electricity costs and climate issues indicated meaningful differences between the clusters were associated with their patterns in electricity consumption changes. These factors provide clues for understanding different responses to high-resolution feedback.

Data availability statement

The data utilized in this study is not available since the authors do not have permissions from the participants to share data with the third parties.

Ethics statement

This study was reviewed and approved by KAIST Institutional Review Board, with the approval number: IRB-21-233.

CRediT authorship contribution statement

Hana Kim: Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Desy Caesary: Writing – review & editing, Writing – original draft, Visualization, Methodology, Formal analysis. Jeongwoo Jang: Writing – review & editing, Project administration, Investigation, Funding acquisition, Conceptualization. Daphne Ngar-yin Mah: Writing – review & editing, Writing – original draft.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Data Collection and Power Planner Installation

To support installation of Power Planner, we made a YouTube video clip about the application and how to install it and sent it to the participants. We also established a communication channel using KakaoTalk and responded promptly to questions. Of the mobilized households, 100 agreed with a handwritten signature to provide 60-min interval electricity information before and after the installation of Power Planner. Because Power Planner installation was initiated in August 2020, data for September and October 2020 and 2021 were compared. We collected electricity consumption data from KEPCO Energy Marketplace (https://bigdata.kepco.co.kr/).Table A1 Statistical description of daily electricity consumption data.

Table A1	Unit	All data	Weekday data	Weekend data	
Hourly electricity consumption data points	–	7182	4977	2205	
Mean	kWh	6.617	6.512	6.853	
Standard deviation	3.545	3.455	3.731	
Minimum	0	0	0	
25 % (25th percentile)	3.892	3.813	4.033	
50 % (median)	6.364	6.298	6.528	
75 % (75th percentile)	8.758	8.624	9.078	
Maximum	26.300	24.961	26.300	

Table A2 Survey questionnaires

Table A2Items	Questions	
quizsum1	During summer (July to August), at what electricity consumption levels does the third tier of the progressive rating system apply?
Choose an incorrect answer regarding the electricity billing system
Choose an activity with the largest electricity consumption of the given items.
Choose an incorrect answer regarding washing machines.
Choose an incorrect answer regarding air conditioning equipment.	
elcharge_gap	How much did you pay for your electricity bill in June 2021?	
elcharge_basic_score	How much of the basic charge did you pay in your electricity bill for June 2021?	
elimportance	Reducing electricity consumption is important to me.	
elinterest	I am quite interested in reducing electricity consumption.	
elrelevance	Reducing electricity consumption is a highly relevant issue to me.	
elefficacy	I can do my best to reduce electricity consumption.	
elshould	I should participate in reducing electricity consumption even if it causes inconvenience.	
climportance	Climate change issue is important to me.	
clinterest	I am quite interested in responses to climate change.	
clrelevance	Climate change is a highly relevant issue to me.	
clefficacy	I can do my best to prevent climate change.	
clshould	I should participate in efforts to prevent climate change, even if it causes inconvenience.	
elact	I am willing to engage in electricity-saving behaviors.	
elsearch	I am willing to seek out additional information on electricity saving.	
eladvice	I am willing to recommend electricity-saving behaviors to my family or acquaintances.	
clact	I am willing to engage in actions to prevent climate change.	
clsearch	I am willing to seek out additional information on climate change.	
cladvice	I am willing to recommend actions to address climate change to my family or acquaintances.	
Note:1 quizsum is a means to measure individuals' daily knowledge levels on their electricity consumption using five questions. If respondents answered correctly all the questions, quizsum will be 5.

Appendix B K-Means Clustering

The K-means algorithm's objective is to minimize the distance from each point (x) to the cluster's center or centroid (v) [62,65], described as:JKm(X,V)=∑i=1c∑j=1nDij2

Dij2 is the distance measure, which generally uses the Euclidean norm of ‖xij−vi‖2 for 1 ≤ i ≤ k and 1 ≤ j ≤ ni,k is the number of clusters, and ni is the number of data points in the ith cluster. The optimal number of clusters (c) is specified and can be determined using various methods, such as the silhouette score and the elbow method. The silhouette score (s) is computed using the mean intra-cluster distance (a) and the mean nearest cluster distance (b) for each data point [97]:s=(b–a)/max(a,b)

An s value close to 1 means the clusters are well separated. After k is determined, the K-means process starts from the random initialization of each centroid. The distance between each data point and the initial centroid is measured, then each data point is assigned to the nearest cluster. The centroid for each cluster is updated by calculating the means of each cluster's data. The data assignment and centroid update are iteratively performed until there is no change in the cluster's centroid. In this study, the K-means used for time series were from the Tslearn python library [98], and the silhouette score was from the Sklearn python library [97].Fig. B1 Result of silhouette score of clustering. Note: (a) weekday, (b) weekend.

Fig. B1

Appendix C Statistical Analysis

C.1. Statistical Analysis of Electricity Consumption Before and After Feedback

A statistical analysis of electricity consumption was conducted to investigate whether there were differences before and after providing feedback to each clustered household through the Power Planner installation. The results of a statistical analysis can support the clustering trend results of K-means. The statistical test began by using Shapiro-Wilk to test the normality of the electricity consumption data. If the electricity consumption data had been from a normal distribution (p > .05), a paired t-test would have been conducted; it was not (p < .05), so a Wilcoxon signed-rank test was conducted to see whether there was any difference in electricity consumption before and after the provision of feedback.

C.2 Statistical Analysis of Household Characteristics Between Clustered Households

A statistical analysis was conducted to investigate the differences in household characteristics between clustered households. The household characteristics information was collected through a survey using various questionnaires for each household (see Table 3). The normality of the survey responses of the clustered households was tested using Shapiro-Wilk. If the data are from a normal distribution (p > .05), either the Welch test (<3 clusters) or a one-way ANOVA test (≥3 clusters) is conducted; if they are not (p < .05), either the Mann-Whitney test (<3 clusters) or Kruskal-Wallis test (≥3 clusters) is conducted to test whether the survey response data are different between the clustered households. Thus, the significance of differences in household characteristics between clustered households can be identified.

Acknowledgements

This work was supported by a 10.13039/501100003725 National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2018R1A5A7025409 ) and by a 10.13039/501100013883 Korea Foundation for the Advancement of Science and Creativity (KOFAC) grant funded by the Korean Ministry of Science & Information and Communication Technology (D23030005 ).
==== Refs
References

1 Chantzis G. The Potential of Demand Response as a Tool for Decarbonization in the Energy Transition 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech) 2022
2 Fischer C. Feedback on household electricity consumption: a tool for saving energy? Energy Efficiency 1 2008 79 104 10.1007/s12053-008-9009-7
3 Buchanan K. Russo R. Anderson B. The question of energy reduction: the problem(s) with feedback Energy Pol. 77 2015 89 96 10.1016/j.enpol.2014.12.008
4 Lee E. From intention to action: habits, feedback and optimizing energy consumption in South Korea Energy Res. Social Sci. 64 2020 10.1016/j.erss.2020.101430
5 Albani A. Domigall Y. Winter R. Implications of customer value perceptions for the design of electricity efficiency services in times of smart metering Inf. Syst. E Bus. Manag. 15 2016 825 844 10.1007/s10257-016-0332-9
6 Asensio O.I. Delmas M.A. Nonprice incentives and energy conservation Proc. Natl. Acad. Sci. U.S.A. 112 2015 E510 E515 10.1073/pnas.1401880112 25583494
7 Azarova V. Reducing household electricity consumption during evening peak demand times: evidence from a field experiment Energy Pol. 144 2020 10.1016/j.enpol.2020.111657
8 Schwartz D. Advertising energy saving programs: the potential environmental cost of emphasizing monetary savings J. Exp. Psychol. Appl. 21 2015 158 25581089
9 Wang Z. How to effectively implement an incentive-based residential electricity demand response policy? Experience from large-scale trials and matching questionnaires Energy Pol. 141 2020 10.1016/j.enpol.2020.111450
10 Delmas M.A. Fischlein M. Asensio O.I. Information strategies and energy conservation behavior: a meta-analysis of experimental studies from 1975 to 2012 Energy Pol. 61 2013 729 739 10.1016/j.enpol.2013.05.109
11 Iweka O. Energy and behaviour at home: a review of intervention methods and practices Energy Res. Social Sci. 57 2019 10.1016/j.erss.2019.101238
12 Nemati M. Penn J. The impact of information-based interventions on conservation behavior: a meta-analysis Resour. Energy Econ. 62 2020 10.1016/j.reseneeco.2020.101201
13 Zangheri Serrenho Bertoldi, energy savings from feedback systems: a meta-studies’ review Energies 12 2019 10.3390/en12193788
14 Chatzigeorgiou I.M. Andreou G.T. A systematic review on feedback research for residential energy behavior change through mobile and web interfaces Renew. Sustain. Energy Rev. 135 2021 10.1016/j.rser.2020.110187
15 Schleich J. Faure C. Klobasa M. Persistence of the effects of providing feedback alongside smart metering devices on household electricity demand Energy Pol. 107 2017 225 233 10.1016/j.enpol.2017.05.002
16 Wang A. Can smart energy information interventions help householders save electricity? A SVR machine learning approach Environ. Sci. Pol. 112 2020 381 393 10.1016/j.envsci.2020.07.003
17 Yang B. Smart metering and systems for low-energy households: challenges, issues and benefits Adv. Build. Energy Res. 13 2017 80 100 10.1080/17512549.2017.1354782
18 Henn L. Taube O. Kaiser F.G. The role of environmental attitude in the efficacy of smart-meter-based feedback interventions J. Environ. Psychol. 63 2019 74 81 10.1016/j.jenvp.2019.04.007
19 Anderson K. Longitudinal analysis of normative energy use feedback on dormitory occupants Appl. Energy 189 2017 623 639 10.1016/j.apenergy.2016.12.086
20 Du L. Guo J. Wei C. Impact of information feedback on residential electricity demand in China Resour. Conserv. Recycl. 125 2017 324 334 10.1016/j.resconrec.2017.07.004
21 Houde S. Real-time feedback and electricity consumption: a field experiment assessing the potential for savings and persistence Energy J. 34 2013 87 102 10.5547/01956574.34.L4
22 Zhou S. Brown M.A. Smart meter deployment in Europe: a comparative case study on the impacts of national policy schemes J. Clean. Prod. 144 2017 22 32 10.1016/j.jclepro.2016.12.031
23 Rose E. Smart meters and federal law: what is the role of federal law in the United States in the deployment of smart electricity metering? Electr. J. 27 2014 49 56 10.1016/j.tej.2014.11.002
24 MOTIE The third basic plan for smart grid (2023-2027) Minitry of Trade, Industry, and Energy 2023
25 KEMRI National and International AMI Installation Trends and Measures to Enhance Profits KEMRI Electricity Economy REVIEW 2020 KEPCO Management Research Institute Najoo Korea 18 31
26 Khanna T.M. A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO2 emissions in residential buildings Nat. Energy 6 2021 925 932 10.1038/s41560-021-00866-x
27 Batalla-Bejerano J. Trujillo-Baute E. Villa-Arrieta M. Smart meters and consumer behaviour: insights from the empirical literature Energy Pol. 144 2020 10.1016/j.enpol.2020.111610
28 Hargreaves T. Nye M. Burgess J. Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term Energy Pol. 52 2013 126 134 10.1016/j.enpol.2012.03.027
29 Nilsson A. Effects of continuous feedback on households' electricity consumption: potentials and barriers Appl. Energy 122 2014 17 23 10.1016/j.apenergy.2014.01.060
30 Li Z. Cao X. Analysis of information feedback on residential energy conservation and the implications: the case of China Front. Environ. Sci. 9 2021 10.3389/fenvs.2021.626890
31 Krishnamurti T. Creating an in-home display: experimental evidence and guidelines for design Appl. Energy 108 2013 448 458 10.1016/j.apenergy.2013.03.048
32 Tiefenbeck V. Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives Nat. Energy 4 2018 35 41 10.1038/s41560-018-0282-1
33 Agarwal R. A review of residential energy feedback studies Energy Build. 290 2023 10.1016/j.enbuild.2023.113071
34 Gneezy U. Rustichini A. Pay enough or don't pay at all Q. J. Econ. 115 2000 791 810
35 Ellegård K. Palm J. Visualizing energy consumption activities as a tool for making everyday life more sustainable Appl. Energy 88 2011 1920 1926 10.1016/j.apenergy.2010.11.019
36 De Dominicis S. Making the smart meter social promotes long-term energy conservation Palgrave Communications 5 2019 10.1057/s41599-019-0254-5
37 Schultz P.W. Using in-home displays to provide smart meter feedback about household electricity consumption: a randomized control trial comparing kilowatts, cost, and social norms Energy 90 2015 351 358 10.1016/j.energy.2015.06.130
38 Bager S. Mundaca L. Making ‘Smart Meters’ smarter? Insights from a behavioural economics pilot field experiment in Copenhagen, Denmark Energy Res. Social Sci. 28 2017 68 76 10.1016/j.erss.2017.04.008
39 Kendel A. Lazaric N. Maréchal K. What do people ‘learn by looking’ at direct feedback on their energy consumption? Results of a field study in Southern France Energy Pol. 108 2017 593 605 10.1016/j.enpol.2017.06.020
40 Vassileva I. Energy consumption feedback devices' impact evaluation on domestic energy use Appl. Energy 106 2013 314 320 10.1016/j.apenergy.2013.01.059
41 Geelen D. The use of apps to promote energy saving: a study of smart meter–related feedback in The Netherlands Energy Efficiency 12 2019 1635 1660 10.1007/s12053-019-09777-z
42 Dillahunt T.R. Mankoff J. Understanding Factors of Successful Engagement Around Energy Consumption between and Among Households Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing 2014 1246 1257
43 Nilsson A. Smart homes, home energy management systems and real-time feedback: lessons for influencing household energy consumption from a Swedish field study Energy Build. 179 2018 15 25 10.1016/j.enbuild.2018.08.026
44 Ayres I. Raseman S. Shih A. Evidence from two large field experiments that peer comparison feedback can reduce residential energy usage J. Law Econ. Organ. 29 2012 992 1022 10.1093/jleo/ews020
45 Liang J. Do energy retrofits work? Evidence from commercial and residential buildings in Phoenix J. Environ. Econ. Manag. 92 2018 726 743 10.1016/j.jeem.2017.09.001
46 Bugden D. Stedman R. A synthetic view of acceptance and engagement with smart meters in the United States Energy Res. Social Sci. 47 2019 137 145 10.1016/j.erss.2018.08.025
47 Fettermann D.C. Getting smarter about household energy: the who and what of demand for smart meters Build. Res. Inf. 49 2020 100 112 10.1080/09613218.2020.1807896
48 Gumz J. Fettermann D.C. What improves smart meters' implementation? A statistical meta-analysis on smart meters' acceptance Smart and Sustainable Built Environment 11 2021 1116 1136 10.1108/sasbe-05-2021-0080
49 Alkawsi G.A. Ali N.a. Baashar Y. An empirical study of the acceptance of IoT-based smart meter in Malaysia: the effect of electricity-saving knowledge and environmental awareness IEEE Access 8 2020 42794 42804 10.1109/access.2020.2977060
50 Hmielowski J.D. The social dimensions of smart meters in the United States: demographics, privacy, and technology readiness Energy Res. Social Sci. 55 2019 189 197 10.1016/j.erss.2019.05.003
51 Tang W. Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data Energy 240 2022 10.1016/j.energy.2021.122500
52 Guo Z. O'Hanley J.R. Gibson S. Predicting residential electricity consumption patterns based on smart meter and household data: a case study from the Republic of Ireland Util. Pol. 79 2022 10.1016/j.jup.2022.101446
53 Ryu D.-H. Kim K.-J. How do households perceive electricity consumption? Evidence from smart metering and survey data in South Korea Energy Res. Social Sci. 92 2022 10.1016/j.erss.2022.102770
54 Brülisauer M. Appliance-specific feedback and social comparisons: evidence from a field experiment on energy conservation Energy Pol. 145 2020 10.1016/j.enpol.2020.111742
55 Puntiroli M. Bezençon V. Feedback devices help only environmentally concerned people act pro-environmentally over time J. Environ. Psychol. 70 2020 10.1016/j.jenvp.2020.101459
56 Matsui K. Ochiai H. Yamagata Y. Feedback on electricity usage for home energy management: a social experiment in a local village of cold region Appl. Energy 120 2014 159 168 10.1016/j.apenergy.2014.01.049
57 MOTIE The 10th Electricity Supply and Demand Basic Plan (2022-2036) 2023 Ministry of Trade, Industry, and Energy Sejong Korea
58 Darby S. Smart metering: what potential for householder engagement? Build. Res. Inf. 38 2010 442 457 10.1080/09613218.2010.492660
59 Chen C.-f. Xu X. Arpan L. Between the technology acceptance model and sustainable energy technology acceptance model: investigating smart meter acceptance in the United States Energy Res. Social Sci. 25 2017 93 104 10.1016/j.erss.2016.12.011
60 Sovacool B.K. Vulnerability and resistance in the United Kingdom's smart meter transition Energy Pol. 109 2017 767 781 10.1016/j.enpol.2017.07.037
61 Goodfellow I. Bengio Y. Courville A. Deep Learning 2016 MIT Press
62 Cebeci Z. Yildiz F. Comparison of K-means and fuzzy C-means algorithms on different cluster structures Journal of Agricultural Informatics 6 2015 10.17700/jai.2015.6.3.196
63 Ahmed M. Seraj R. Islam S.M.S. The k-means algorithm: a comprehensive survey and performance evaluation Electronics 9 2020 10.3390/electronics9081295
64 Oti E.U. Comprehensive review of K-means clustering algorithms International Journal of Advances in Scientific Research and Engineering 7 2021 64 69 10.31695/ijasre.2021.34050
65 MacQueen J. Some Methods for Classification and Analysis of Multivariate Observations 5th Berkeley Symp. Math. Statist. Probability 1967
66 Amri Y. Analysis clustering of electricity usage profile using K-means algorithm IOP Conf. Ser. Mater. Sci. Eng. 105 2016 012020 10.1088/1757-899X/105/1/012020
67 Czétány L. Development of electricity consumption profiles of residential buildings based on smart meter data clustering Energy Build. 252 2021 10.1016/j.enbuild.2021.111376
68 Tureczek A. Nielsen P.S. Madsen H. Electricity consumption clustering using smart meter data Energies 11 2018 859 10.3390/en11040859
69 Kim S.H. Lee J.W. Electricity consumption pattern analysis of nationwide apartment using clustering method based on open data Journal of KIABES 15 2021 537 548 10.22696/JKIAEBS.20210045
70 Kim Y.I. Ko J.M. Choi S.H. Methods for Generating TLPs (Typical Load Profiles for Smart Grid-Based Energy Programs 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) 2011 10.1109/CIASG.2011.5953331
71 Rhodes J.D. Clustering analysis of residential electricity demand profiles Appl. Energy 135 2014 461 471 10.1016/j.apenergy.2014.08.111
72 Kwac J. Flora J. Rajagopal R. Household energy consumption segmentation using hourly data IEEE Trans. Smart Grid 5 2014 420 430 10.1109/TSG.2013.2278477
73 Räsänen T. Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data Appl. Energy 2010 3538 3545 10.1016/j.apenergy.2010.05.015
74 Zhou K.-l. Yang S.-l. Shen C. A review of electric load classification in smart grid environment Renew. Sustain. Energy Rev. 24 2013 103 110 10.1016/j.rser.2013.03.023
75 Panapakidis I.P. Alexiadis M.C. Papagiannis G.K. Electricity Customer Characterization Based on Different Representative Load Curves 2012 9th International Conference on the European Energy Market 2012 10.1109/EEM.2012.6254761
76 Toussaint W. Moodley D. Clustering residential electricity consumption data to create archetypes that capture household behaviour in South Africa, South African Comput. J. 32 2020 10.18489/sacj.v32i2.845
77 Virtanen P. SciPy 1.0: fundamental algorithms for scientific computing in Python Nat. Methods 17 2020 261 272 32015543
78 Aydin E. Brounen D. Kok N. Information provision and energy consumption: evidence from a field experiment Energy Econ. 71 2018 403 410 10.1016/j.eneco.2018.03.008
79 Grønhøj A. Thøgersen J. Feedback on household electricity consumption: learning and social influence processes Int. J. Consum. Stud. 35 2011 138 145 10.1111/j.1470-6431.2010.00967.x
80 Jessoe K. Rapson D. Knowledge is (less) power: experimental evidence from residential energy use Am. Econ. Rev. 104 2014 1417 1438 http://www.jstor.org/stable/42920745
81 Lynham J. Why does real-time information reduce energy consumption? Energy Econ. 54 2016 173 181 10.1016/j.eneco.2015.11.007
82 Gans W. Alberini A. Longo A. Smart meter devices and the effect of feedback on residential electricity consumption: evidence from a natural experiment in Northern Ireland Energy Econ. 36 2013 729 743 10.1016/j.eneco.2012.11.022
83 Carroll J. Lyons S. Denny E. Reducing household electricity demand through smart metering: the role of improved information about energy saving Energy Econ. 45 2014 234 243 10.1016/j.eneco.2014.07.007
84 Matsukawa I. Information acquisition and residential electricity consumption: evidence from a field experiment Resour. Energy Econ. 53 2018 1 19 10.1016/j.reseneeco.2018.02.001
85 Karlin B. Zinger J.F. Ford R. The effects of feedback on energy conservation: a meta-analysis Psychol. Bull. 141 2015 1205 1227 10.1037/a0039650 26390265
86 Trotta G. Electricity awareness and consumer demand for information Int. J. Consum. Stud. 45 2020 65 79 10.1111/ijcs.12603
87 Composto J.W. Weber E.U. Effectiveness of behavioural interventions to reduce household energy demand: a scoping review Environ. Res. Lett. 17 2022 10.1088/1748-9326/ac71b8
88 Novan K. Smith A. The incentive to overinvest in energy efficiency: evidence from hourly smart-meter data Journal of the Association of Environmental and Resource Economists 5 2018 577 605 10.1086/697050
89 Schultz P.W. The constructive, destructive, and reconstructive power of social norms Association for Psychological Science 18 2007 429 434 10.1177/1745691617693325
90 Tiefenbeck V. Overcoming salience bias: how real-time feedback fosters resource conservation Manag. Sci. 64 2018 1458 1476 10.1287/mnsc.2016.2646
91 Kavousian A. Rajagopal R. Fischer M. Determinants of residential electricity consumption: using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior Energy 55 2013 184 194 10.1016/j.energy.2013.03.086
92 Tendenvall M. Mundaca L. Behaviour, Context and Electricity Use: Exploring the Effects of Real-Time Feedback in the Swedish Residential Sector 39th IAEE International Conference ‘Energy: Expectations and Uncertainty’ 2016
93 Solution Seoul Eco-mileage system [cited 2023 08.14.]; Available from: https://seoulsolution.kr/en/content/eco-mileage-system-1 2023
94 Han P. Impact of climate change beliefs on youths' engagement in energy-conservation behavior: the mediating mechanism of environmental concerns Int J Environ Res Public Health 19 2022 10.3390/ijerph19127222
95 Kim M.-J. Determining the relationship between residential electricity consumption and factors: case of Seoul Sustainability 12 2020 10.3390/su12208590
96 von Borgstede C. Andersson M. Johnsson F. Public attitudes to climate change and carbon mitigation—implications for energy-associated behaviours Energy Pol. 57 2013 182 193 10.1016/j.enpol.2013.01.051
97 Pedregosa F. Scikit-learn: machine learning in Python J. Mach. Learn. Res. 12 2011 2825 2830
98 Tavenard R. Tslearn, A machine learning toolkit for time series data J. Mach. Learn. Res. 21 2020 1 6 34305477
