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Article

Residential Electricity Consumption Behaviors in Eastern Romania: A Non-Invasive Survey-Based Assessment of Consumer Patterns

by
Codrin Donciu
,
Elena Serea
* and
Marinel Costel Temneanu
Faculty of Electrical Engineering, “Gheorghe Asachi” Technical University of Iași, 700050 Iași, Romania
*
Author to whom correspondence should be addressed.
Energies 2025, 18(18), 4883; https://doi.org/10.3390/en18184883
Submission received: 22 August 2025 / Revised: 8 September 2025 / Accepted: 12 September 2025 / Published: 14 September 2025
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Abstract

This study investigates residential electricity consumption behaviors in the Moldova region of Romania, with a focus on identifying consumption patterns through a non-invasive, survey-based approach. Unlike intrusive monitoring or smart metering methods, the survey collected detailed self-reported data on appliance use, time-of-use awareness, and household characteristics across 55 residential units. The analysis introduced an error-based metric comparing calculated and billed consumption, modeled under a normal distribution to assess estimation accuracy. Results reveal a stable dominance of mid-range consumption bands, alongside emerging stratification, with an increasing share of households transitioning to higher consumption levels. Appliance-level analyses highlight systematic underestimation of high-load devices, such as washing machines and HVAC systems, reflecting perceptual gaps in consumer awareness. Furthermore, demographic profiling indicates that in many households, high-duration and high-load consumers differ, with women more frequently assuming dual roles in energy-intensive tasks within the traditional Eastern European context. The findings demonstrate the potential of non-invasive survey methods to capture behavioral dimensions of energy use that remain underexplored in the absence of smart metering infrastructure, offering new insights into demand-side heterogeneity in peripheral EU regions.

1. Introduction

Based on the recognition that energy efficiency policies alone are insufficient to reverse the increasing demand for environmentally harmful energy services, the concept of energy sufficiency was first introduced by Darby and Fawcett in 2018 [1]. Energy sufficiency describes a condition in which people’s basic needs for energy services are met equitably, while staying within ecological limits. As a policy approach, sufficiency extends the range of demand-side options beyond efficiency improvements. Though recently acknowledged in the 2023 IPCC report [2], sufficiency has only marginally influenced EU energy policy. An analysis of 2019–2023 national energy plans in Denmark, France, Germany, and Italy shows that most sufficiency measures focused on substitution rather than absolute demand reduction [3].
In Romania, significant progress has been made in aligning energy policy with EU directives, including the rollout of smart metering, promotion of energy communities, and incentives for energy-efficient renovations [4]. Reflecting these policy advancements, Figure 1 displays the distribution of residential electricity consumption from 2017 to 2024 in Romania, categorized by annual consumption bands. Some key aspects arise here:
  • Stability in mid-range consumption: The 1000–2499 kWh and 2500–4999 kWh bands have consistently accounted for the majority share (about 60–70%) across all years, indicating that most Romanian residential consumers fall within moderate consumption brackets.
  • Slight decline in low consumption: The share of residential consumption under 1000 kWh annually has declined slightly over time, suggesting reduced prevalence of minimal energy use.
  • Rising high consumption segments: The shares of the 5000–14,999 kWh and ≥15,000 kWh categories have gradually increased since 2017, indicating a growing portion of high-consumption.
  • Post-2020 stabilization: After noticeable shifts around 2019–2020 (likely due to COVID-19-related behavioral changes), the consumption distribution has largely stabilized, with only minor annual variation.
The trend suggests a gradual polarization in electricity usage patterns, with increasing energy demands in higher bands. This emerging tendency reveals also increased behavioral differentiation among residential consumers in terms of electricity use. While many maintain moderate consumption patterns, a growing number exhibit high-consumption behaviors, potentially influenced by lifestyle changes, increased ownership of electrical devices, or shifts in heating and cooling practices. These behavioral differences often align with variations in income, awareness, and access to energy-efficient technologies [5]. As a result, understanding and segmenting consumer behavior becomes crucial for crafting targeted demand-side interventions and fostering more sustainable energy practices.
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Figure 1. Residential electricity usage in Romania by consumption categories, Statista 2025 [6].
Figure 1. Residential electricity usage in Romania by consumption categories, Statista 2025 [6].
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Nonetheless, behavioral aspects of residential electricity use remain understudied, particularly in less urbanized or economically peripheral regions such as Moldova (Romania). With regional disparities in infrastructure, education, and income, consumer behavior in these areas may diverge significantly from European level averages. Residential behavior is shaped by psychological factors, including personal norms, perceived responsibility, and values [7]. Survey-based studies have shown that consumers with strong environmental values and a sense of efficacy are more likely to engage in energy-saving behavior, even in the absence of immediate financial incentives [8]. In parallel, gamification and behaviorally designed feedback tools have demonstrated success in increasing user attention and pro-environmental action [9,10].
Consumer awareness of personal electricity use remains limited, and empirical studies show that households often misjudge the relative contributions of different appliances [11]. This perceptual gap constrains the effectiveness of both efficiency and sufficiency measures, since accurate behavioral adjustment requires a realistic understanding of end-use contributions [12,13].
Recent meta-analyses suggest that standalone technological interventions such as installing smart meters typically yield only modest reductions in residential electricity consumption (approximately 3.4%) [14], while appliance-level feedback can save from 3% to 18% annual energy consumption for the entire house [15]. This latter monitoring technique is known as non-intrusive appliance load monitoring (NILM) or load disaggregation, referring to a set of signal processing and machine learning techniques used to estimate the individual energy consumption of appliances by using the total energy consumption in the building collected using a single sensor [16]. NILM faces several key limitations that affect its effectiveness in real-world applications. These include the low resolution of data, which hampers the accurate identification of appliance-level usage and difficulties in distinguishing overlapping or simultaneous appliance events. Many devices exhibit similar power signatures or variable operation patterns, further complicating disaggregation. Additionally, the lack of labeled training datasets limits algorithm performance and generalizability, while electrical noise and diverse household appliance behaviors introduce further uncertainty [17]. Scalability is often constrained by differences in infrastructure and limited user engagement and privacy concerns [18]. Collectively, these challenges reduce NILM’s reliability, especially in uncontrolled residential environments.
This study addresses the attitudinal dimensions of electricity use by applying a non-invasive, survey-based method to explore electricity consumption applied to a group of residential consumers in Moldova (Romania). Unlike advanced NILM approaches that rely on machine learning and high-frequency data, the present study introduces a non-invasive, survey-based framework aimed at uncovering consumer awareness, estimation biases, and behavioral heterogeneity in residential electricity use. The contribution lies not in technical load disaggregation, but in providing a complementary perspective on residents’ consumption. This behavioral lens is particularly valuable in contexts such as Eastern Romania, where advanced metering coverage remains uneven.
The central research question is: Can discernible behavioral patterns be identified among residential electricity consumers in Moldova (Romania) using a non-invasive, survey-based method?

2. Materials and Methods

2.1. Study Design and Sample

It is important to clarify that the present work does not attempt to replicate or extend advanced non-intrusive load monitoring (NILM) techniques, which often rely on machine learning or high-resolution event disaggregation. Instead, our approach applies a non-invasive, survey-based framework combined with statistical error analysis to capture behavioral and perceptual aspects of electricity consumption. This positions the method as complementary to NILM: while NILM excels in identifying appliance-level loads from aggregate signals, the survey approach reveals user awareness, estimation biases, and socio-demographic patterns that cannot be extracted from metering data alone. The methodology is thus designed as a lightweight, context-appropriate alternative for regions where advanced metering infrastructure is limited.
The research was based on a non-invasive, questionnaire-based survey designed to gather detailed data on individual and household-level electricity consumption. The survey was applied to a purposive sample of 55 residential electricity consumers from both urban and rural areas in eastern area of Romania. The sample was selected to ensure diversity in dwelling type (house/apartment), household size, socio-economic status, and appliance ownership.

2.2. Data Collection Instrument

A structured survey instrument (simplified excerpt in Figure 2) was developed to capture the following:
  • Sociodemographic characteristics (age, gender, education level, employment status).
  • Dwelling characteristics (type, area in m2, number of occupants, urban/rural location).
  • Appliance ownership and self-declared usage (hours/month) for all electricity-consuming devices.
  • Billed consumption values from electricity bills over the same period. Billed consumption was considered as the quantity effectively read on the power meter by the energy supplier, taken directly from the bill document (for the same period the calculation assessment was recorded).
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Figure 2. Excerpt of a survey completed with monthly declared aggregated consumption.
Figure 2. Excerpt of a survey completed with monthly declared aggregated consumption.
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The survey was designed to distinguish lighting circuit receivers from power circuit receivers for accuracy. Each respondent provided individualized usage data per appliance, allowing for disaggregated energy behavior analysis. The total monthly electricity consumption was automatically calculated by the instrument at the levels of residential unit, individual occupants, and each appliance/receiver, along with the corresponding total hours of usage.

2.3. Data Validation

To ensure consistency and accuracy, estimated consumption values were computed from declared usage using known appliance power ratings. Bills containing estimated consumptions (7 cases) were excluded from the error analysis and marked as N/A. Cross-checks were performed to validate reports against typical operational parameters, using the model, manufacturing year, and product code provided by respondents. For washing machines and refrigerators, respondents also submitted photographs of the appliance label, which enabled verification of declared energy against usage hours; these checks served as internal plausibility validation.

2.4. Adjustments for Simultaneous Usage

To ensure a realistic allocation of electricity consumption among cohabitants in cases of shared appliance use, a weighting adjustment was applied. This was necessary when the sum of individually declared usage durations exceeded the plausible operational time of an appliance. The adjustment was performed as follows:
  • Aggregation: For each appliance, the individually declared daily usage durations were summed across all household members.
  • Normalization: The appliance’s total realistic daily operation time was divided by the sum obtained above, resulting in a subunitary coefficient (i.e., <1).
  • Adjustment: Each individual’s declared usage duration was then multiplied by this coefficient to obtain the adjusted value used in the per-person consumption calculation.
This correction accounts for overlapping usage patterns and prevents overestimation of appliance runtime (such as refrigerators, lighting, and entertainment devices). This provides a more accurate representation of individual electricity consumption, particularly in multi-occupant households.

2.5. Consumption Estimation and Error Analysis

Calculated consumption was derived for each individual and appliance, and then the assessment error was calculated. This enabled a comparative analysis between self-reported and billed consumption data.

2.6. Statistical Processing

Descriptive statistics (mean, standard deviation, skewness, and kurtosis) were computed for the assessment error. This approach facilitates a rigorous understanding of uncertainty in consumption reporting and supports the development of improved energy demand assessment methodologies.

3. Results

3.1. Residential Consumer Own Consumption Assessment

Table 1 presents a comparative analysis between the calculated and billed electricity consumption for a sample of Romanian residential consumers, sorted in ascending order by the consumption assessment error (ε). This was computed as the relative deviation between the calculated and billed values, normalized to the billed consumption, and expressed as a percentage.
ε = (calculated consumption − billed consumption) × 100/billed consumption
This metric provides insight into the accuracy of indirect consumption estimation methods, which are commonly applied in the absence of advanced metering infrastructure. The statistical characterization of ε, including its mean and standard deviation σ, allows for the application of the normal distribution to model the probability density of error occurrence. NORMAL returns the height of the normal probability density function at the point ε. y represents the estimated number of consumers corresponding to each error bin. This value is computed as follows:
y = f(ε) × N × Δε
where
f(ε) is the height of the normal probability density function at a given error value;
N = 55 is the total number of consumers in the sample;
Δε = 5% is the bin width used for grouping error values.
y approximates the frequency density of consumers within each error bin, under the assumption that the distribution of assessment errors follows a normal distribution. This facilitates visualization and analysis of how common certain levels of estimation error are within the population facilitates the assessment of the reliability of non-metered consumption estimates.
Table 1. Statistical evaluation of residential electricity consumption accuracy.
Table 1. Statistical evaluation of residential electricity consumption accuracy.
ConsumerCalculated Consumption (kWh)Billed Consumption (kWh)ε (%)Mean (%)σNORMALy
1253.2204334−24.18558.555231.38910.0073772.028678
2110.076139−20.8086 0.00820542.2564909
3152.55185−17.5405 0.00899592.473882
4160.4928183−12.2990 0.01019252.8029484
572.895983−12.1735 0.01021962.8103812
6342.381387−11.5294 0.01035682.8481216
787.95599−11.1565 0.01043512.869651
854.910561−9.9827 0.01067562.9357832
9232.23255−8.9294 0.01088312.9928629
10270.66289−6.3460 0.01135523.1226693
11365.22389−6.1131 0.01139493.133602
12209.6392220−4.7094 0.01162393.196577
13160.2309167−4.0533 0.01172453.2242324
14308.055320−3.7328 0.01177213.2373161
15163.728170−3.6894 0.01177843.2390657
1684.936187−2.3722 0.01196233.2896268
17150.279153−1.7784 0.01203923.3107694
18245.8102250−1.6759 0.01205213.3143139
19455462−1.5151 0.01207353.3202188
20378.18381−0.7401 0.01216433.3451884
21337.28903370.0857 0.01225523.3701894
22204.3512040.1720 0.01226433.3726771
23486.0724801.2650 0.01237143.4021237
2481.0998801.3748 0.01238133.404868
25154.0351493.3791 0.01253793.4479326
26104.15441004.1544 0.01258533.4609479
27139.581344.1641 0.01258583.4610981
28208.5452004.2725 0.01259183.4627487
29131.71641264.5368 0.01260583.4666066
3080.727774.8402 0.01262093.4707371
31137.581305.8307 0.01266183.4819894
32159.11251506.0750 0.01266523.4842363
33143.251356.1111 0.01267113.4845507
34149.66161416.1429 0.01267213.4848244
35166.89711576.3039 0.01267693.4861518
36373.51533910.1814 0.01269253.4904427
37128.56211511.7930 0.01264213.4765851
38459.42407.512.7411 0.01259713.4641901
3998.3258713.0172 0.01258183.4599946
40117.610810017.6108 0.01219153.3526671
41274.7123317.9012 0.01215853.3435848
42167.881514218.2264 0.01212043.3331103
43133.57510922.5458 0.01150783.1646393
44116.9649523.12 0.01141243.1384196
45620.17541051.2621 0.00503691.3851407
4673.9654660.7934 0.00318210.8750852
47109.45976373.7455 0.00147070.4044327
48180.19564181.5546 3.222 × 10−98.859 × 10−7
49243.987NA
50103.47NA
51303.882NA
52572.46NA
53172.191NA
54303.366NA
551093.65NA
In Table 2, ε represents the percentage error in consumption assessment, calculated in Table 1 from a sample of 48 residential consumers. The statistical analysis reveals high kurtosis and positive skewness, indicating a distribution characterized by extreme outliers and a pronounced rightward asymmetry. Figure 3 visually represents this spread of errors, with the empirical data points overlaid by the theoretical normal distribution curve derived from the sample’s mean and variance. The significant deviation from normality is further supported by the Kolmogorov–Smirnov test results (p < 0.01), confirming that the observed error distribution does not completely follow a normal pattern.
Unlike the mean and median, the mode is not affected by extreme values (outliers), so it is particularly useful. A mode of 1.265% suggests that many residential consumers estimated their consumption fairly accurately, with small error, even though a few others made large over- or underestimations.
Figure 4 displays the histogram of percentage errors in own consumption assessment for the 48 residential consumers, overlaid with the normal distribution curve based on the sample’s mean (8.555) and standard deviation (31.389). Although the distribution exhibits positive skewness (3.976) and high kurtosis (19.913), pointing to the presence of outliers, most responses cluster near the median (2.377), suggesting reasonable accuracy in the majority of cases. The extreme deviations are likely attributable to subjective estimations, reflecting personal perceptions or misunderstandings of actual energy use rather than systematic errors. These findings offer insight into residential consumption estimation and highlight opportunities for improving consumer awareness regarding their actual behavior.
The normal distribution curve is shown only as a reference baseline to highlight skewness and deviations in error behavior; it is not applied as an inferential model for uncertainty quantification.

3.1.1. Residential Consumers’ Declared Consumption by Appliances

Table 3 presents disaggregated electricity consumption data for key household appliances in residential settings across the northeastern region of Romania. The table includes individual consumption values (in a random order), sample means computed from the surveyed consumers -μsample, and the corresponding average consumption figures reported by the local electricity supplier—μsupplier [19,20]. The selected end uses (lighting, washing machines, refrigerators, vacuum cleaners, cookers/ovens, PCs/laptops, and HVAC systems) represent the most significant or commonly used appliances within residential consumers [21,22]. This comparative framework facilitates the assessment of consumption behavior at the appliance level, helping to identify usage patterns, potential under- or over-reporting in self-assessments, and opportunities for targeted energy efficiency interventions tailored to regional demand characteristics.
Figure 5 presents a comparative analysis of estimated electricity consumption (μsample) versus supplier-reported averages (μsupplier) across key household appliances, including lighting, washing machines, refrigerators, vacuum cleaners, cookers/ovens, PCs/laptops, and HVAC systems. Each chart illustrates the discrepancy between consumer-reported usage and reference values provided by energy suppliers, offering insight into residential consumers’ awareness, estimation accuracy and potential areas of consumption misperception. All values are expressed in kWh and represent average monthly consumption per device category.
The graph in Figure 6 shows monthly energy data, where the refrigerators (1739.39 kWh) and lighting (1692.37 kWh) dominate residential electricity use due to continuous or widespread operation. HVAC (1494.54 kWh) and cooking appliances (1423.80 kWh) also contribute substantially, reflecting high thermal loads (mainly because the reference period was April). Washing machines (1237.28 kWh) exhibit significant consumption, primarily from water heating cycles. Computing devices (754.79 kWh) and vacuum cleaners (360.64 kWh) show lower energy demand, consistent with their usage profiles.
The graph in Figure 7 shows the total operating time (h/month) for main appliances, where lighting (37,442.36 h) and refrigerators (11,937.3 h) exhibit the highest operating durations, aligning with their continuous and multi-user use. Computers/laptops (6384.76 h) show extended activity, while HVAC (2078.4 h) and washing machines (2066.59 h) indicate shorter but energy-intensive cycles. Cooking appliances (1062.58 h) and vacuum cleaners (474.8 h) have low usage frequency. These runtime patterns, when cross-referenced with the monthly energy consumption above, inform on behavioral load management proper strategies.

3.1.2. Residential Consumers’ Profile Analysis

From each completed survey with contextual factors, such as location (urban/rural), dwelling type (house/apartment), floor area (m2), and number of occupants, the declared electricity consumption was calculated for every occupant at the place of consumption. The highest-duration consumer in each residential unit was identified, and their characteristics were extracted: gender, age, occupation, education level, and total individual electricity usage (h/month). Similarly, for the highest-load consumer was identified, extracting the same set of personal and contextual attributes, along with their total individual electricity consumption (kWh/month). A summary of these characteristics is presented in Table 4.
As shown in Figure 8, the histograms of the highest-duration consumers and highest-load consumers exhibit similar distributions by age and gender, despite a 34.5% discrepancy between individuals—meaning that in 19 of the 55 residential units, the highest-duration consumer was not the same as the highest-load consumer. Among the remaining 36 cases, where the same individual was both the highest-load and highest-duration consumer, the lowest hourly specific consumption was observed in a rural house of 122 m2 with four occupants, where the consumer was a female aged 36–65, unemployed, and had a medium-level education. Conversely, the highest hourly specific consumption occurred in a rural house of 150 m2 with five occupants, where the consumer was also a female aged 36–65, unemployed, but with higher education.

4. Discussion

The analysis of residential consumers own electricity consumption assessment errors reveals the following:
1.
Systematic over- or underestimation.
  • The mean error of +8.55% indicates that, on average, the calculated consumption overestimates what is actually billed. This suggests a potential systemic bias in estimation methods, due to the following:
    • Conservative estimation during meter read gaps.
    • Lack of real-time data (e.g., smart meters not yet fully deployed).
    • Behavioral changes (e.g., seasonal efficiency improvements, reduced usage) not captured by static estimation models.
2.
High variability and skewness.
  • The standard deviation of ~31% and strong positive skewness (3.97) imply that while most residential consumers are close to their actual consumption, a minority exhibit very large overestimations—some over 180%. These outliers could reflect the following:
    • Residential consumers with erratic or seasonal usage patterns (e.g., electric heating/cooling).
    • Inaccurate estimation algorithms not accounting for recent behavioral shifts (e.g., PV panels installation).
    • Data entry or billing errors.
3.
Behavioral insight
  • The dispersion and positive tail of the error suggest behavioral heterogeneity—some consumers may drastically change their consumption (e.g., buying new appliances, teleworking), while others are relatively stable. Estimation models that do not adapt to such behavioral dynamics lead to higher uncertainty.
  • Implications for energy management and policy rely on
    • Trust and perception: Repeated overbilling based on overestimated consumption may undermine consumer trust in energy providers and reduce engagement with energy-saving initiatives.
    • Targeting efficiency programs: The divergence in error rates suggests that residential consumers differ in how predictable their consumption is, possibly linked to income level, dwelling characteristics or usage behavior. Tailored feedback or interventions may be more effective than one-size-fits-all approaches.
    • Smart metering justification: These findings underscore the need for high-resolution, real-time data (e.g., from smart meters) to reduce estimation errors and better capture consumption behaviors.
The analysis of disaggregated consumption for key household appliances reveals the following:
The error/difference between the mean self-calculated consumption and the mean supplier-metered consumption by common appliances is higher for washing machines (−55%), which are primarily inductive loads with variable operating cycles. The lowest error is for cookers/ovens (0.97%), which are primarily resistive loads with constant operating cycles.
The other discrepancies (related to the mean consumption measured by the energy supplier) can be attributed to a combination of behavioral, technical, and contextual factors:
  • In cases where multiple individuals use the same luminaires, the reported consumption reflects a cumulative (summative) value rather than an individualized (inclusive) allocation per user.
  • The underestimation of HVAC consumption can be attributed to the variable and often automated nature of its operation. Consumers may underestimate total runtime, particularly during transitional seasons or when thermostats automate cycling. Moreover, the efficiency of HVAC systems varies widely with equipment type and insulation quality, complicating accurate perception.
  • Despite being continuously operational, refrigerators tend to be overlooked in consumer estimations due to their quiet, background function. Underestimation may also result from improved energy efficiency in modern models, contrasting with outdated perceptions of refrigerator consumption.
The analysis of residential consumers’ profiles reveals that in 34.5% cases, the highest-duration consumer is different from the highest-load consumer, with homogenous gender, age, and education profiles. This indicates that environmental and socio-demographic factors exert limited influence, pointing instead to behavioral typologies as the primary differentiator.
Across the sample, the highest-duration consumers were predominantly in ISCED 2–3 (lower/upper secondary). This aligns with evidence that lower energy literacy is associated with less accurate mental models of end-use and routine, time-intensive practices, which can inflate hours of use without necessarily raising instantaneous load. Reviews of household energy literacy document persistent gaps in end-use understanding among lower-education groups, with implications for behavior and persistence of habitual practices [23]. By contrast, the highest-load consumers appeared more frequently among ISCED 6–7 (tertiary) respondents, who were often students or younger adults. This pattern is consistent with the diffusion of ICT/entertainment devices and more power-dense appliance portfolios in higher-education/younger cohorts, producing lower use time but higher consumption via PCs, gaming, multiple displays, and HVAC set points.
Rather than limiting the applicability of behavioral segmentation, this educational stratification demonstrates the need for differentiated demand-side management: (i) for ISCED 2–3, interventions that raise device-specific literacy and re-structure routines (timers, default shut-off, targeted prompts); (ii) for ISCED 6–7, measures that address high-load end uses (efficient ICT setups, power management, thermal set points) and provide appliance-level feedback that corrects misperceptions about load contributions. This targeted approach strengthens, rather than restricts, the universality of segmentation by ensuring that interventions remain effective across heterogeneous education levels.
Overall, these results underscore a general trend: appliances with visible, short-duration use tend to be estimated more accurately, while those with automated, background or energy-intensive components are more likely to be misunderstood. This highlights the importance of consumer education to foster energy-conscious behavior. Recent studies show that NILM-assisted HEMS [24] can combine appliance-level insights with optimization strategies for flexible assets, highlighting the value of user-centric design in residential energy management.
A limitation of the present survey is that it captures cumulative monthly loads rather than time-of-use patterns; however, this was an intentional trade-off to preserve non-invasiveness. Future extensions could incorporate time-use diaries, smart plugs, or NILM monitoring to capture intra-day dynamics.
In the Eastern European socio-cultural context, persistent traditional gender roles significantly influence household electricity consumption patterns. Empirical observations indicate that female occupants are identified as both the highest-duration and highest-load consumers in 75% cases. This is largely attributable to their disproportionate involvement in energy-intensive domestic activities, such as cooking, laundering, and cleaning, tasks that require sustained and diversified appliance use. This finding underscores the need for regionally adapted segmentation strategies. In the Eastern European context, traditional gendered divisions of household labor intensify women’s involvement in energy-intensive tasks, limiting the applicability of universal behavioral segmentation schemes. Therefore, demand-side management interventions should account for cultural and socio-demographic contexts, rather than relying on standardized segmentation models. Conversely, younger male occupants, while often classified as the highest-duration consumers, typically engage with single-purpose devices, such as TV, games, or computers, contributing less to overall load intensity. This behavioral segmentation reflects entrenched domestic labor divisions and suggests that electricity usage in residential settings is strongly shaped by socio-demographic roles and responsibilities. Understanding these patterns is particularly relevant for improving the accuracy of NILM, as behavioral context can enhance appliance-level disaggregation, inform user-specific load signatures, and reduce classification errors in complex consumption profiles. Recent AI-based energy advisors (e.g., RHEA frameworks [25]) highlight the role of real-time analytics, while they require reliable behavioral baselines. This study complements such approaches by revealing awareness gaps and socio-demographic patterns that can guide future AI-driven demand-side tools.

5. Conclusions

This study investigated residential electricity consumption behavior in Eastern Romania through a non-invasive, survey-based methodology. The analysis revealed discrepancies between declared and billed consumption, with errors distributed across both underestimation and overestimation cases. The statistical characterization of these errors, combined with appliance-level and occupant-specific assessments, provides a nuanced picture of how electricity is perceived, allocated, and actually consumed in households. These results, while not yet generalizable, provide a strong proof-of-concept for non-invasive survey methods in capturing context-specific consumption behaviors. Future research should extend this pilot’s scope by incorporating comparative analyses with Western EU contexts in order to better understand how cultural, infrastructural, and technological differences shape demand-side heterogeneity.
A key contribution lies in identifying distinct behavioral roles within the same household: high-load consumers, responsible for the largest share of kWh use, and high-duration consumers, characterized by extended appliance usage time. While these roles coincided in some cases, they diverged in over one-third of households, showing that electricity demand is shaped not only by appliance ownership but also by user behavior and activity patterns.
Beyond its empirical findings, this study contributes to the literature in several distinctive ways. First, it demonstrates that non-invasive, survey-based methods can yield complementary insights into residential electricity behavior (particularly regarding awareness gaps and behavioral heterogeneity that are not captured by conventional monitoring approaches), offering a socially acceptable alternative in contexts where advanced metering or NILM infrastructures are limited. Second, by distinguishing between high-load and high-duration consumers, the analysis uncovers a behavioral segmentation within households that has direct implications for targeted demand-side management strategies. While limited to monthly loads, the approach offers a scalable basis that can be extended with time-use diaries or smart devices to capture intra-day dynamics. Third, the focus on Eastern Romania provides much-needed empirical evidence from an underrepresented region, where social roles, income distribution, and infrastructural disparities shape distinct consumption dynamics compared to Western Europe. Finally, the revealed gap between declared and actual consumption highlights persistent shortcomings in consumer awareness, underscoring the importance of behavioral and educational measures to complement technical interventions.
Collectively, these findings suggest that effective energy policy must integrate technical, behavioral, and educational dimensions. While efficiency and sufficiency remain essential, their success depends on bridging the perceptual gap in consumer awareness and customizing interventions to the heterogeneous behavioral roles observed within households.

Author Contributions

Conceptualization, C.D. and E.S.; methodology, C.D. and E.S.; software, C.D. and E.S.; validation, E.S. and M.C.T.; formal analysis, C.D.; investigation, C.D. and E.S.; resources, M.C.T.; data curation, M.C.T.; writing—original draft preparation, E.S.; writing—review and editing, C.D. and E.S.; project administration, M.C.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by North-East Regional Programme 2021–2027, PR/NE/2024/P1/RSO1.1_RSO1.3/1, through grant “Integrated digital smart home system for ensuring energy savings, security, and comfort in residences”, code 338255.

Data Availability Statement

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 3. Distribution of own consumption assessment errors (ε) and corresponding normal distribution curve (orange).
Figure 3. Distribution of own consumption assessment errors (ε) and corresponding normal distribution curve (orange).
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Figure 4. Histogram with normal distribution curve for own consumption assessment error.
Figure 4. Histogram with normal distribution curve for own consumption assessment error.
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Figure 5. Estimated own consumption vs. average metered consumption in the grid (supplier)—a multi-appliance comparison.
Figure 5. Estimated own consumption vs. average metered consumption in the grid (supplier)—a multi-appliance comparison.
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Figure 6. Aggregated monthly electricity consumption (kWh).
Figure 6. Aggregated monthly electricity consumption (kWh).
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Figure 7. Cumulative operating time (h) for major residential appliances.
Figure 7. Cumulative operating time (h) for major residential appliances.
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Figure 8. Demographic distribution of highest-duration and highest-load electricity consumers.
Figure 8. Demographic distribution of highest-duration and highest-load electricity consumers.
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Table 2. Statistical parameters of electricity consumption assessment error.
Table 2. Statistical parameters of electricity consumption assessment error.
ParameterValue
Mean8.555
Standard Error4.531
Median2.377
Mode1.265
Standard Deviation (σ)31.389
Sample Variance985.278
Kurtosis19.913
Skewness3.976
Range205.740
Minimum−24.186
Maximum181.555
Sum410.651
Count (n)48
Kolmogorov–Smirnov D-Statistic0.258
p-Value<0.01
Table 3. Disaggregated electricity consumption (kWh) per appliance (A1—lighting, A2—washing machine, A3—refrigerators, A4—vacuum cleaner, A5—cooker/oven, A6—PC/laptop, A7—HVAC) across surveyed residential consumers.
Table 3. Disaggregated electricity consumption (kWh) per appliance (A1—lighting, A2—washing machine, A3—refrigerators, A4—vacuum cleaner, A5—cooker/oven, A6—PC/laptop, A7—HVAC) across surveyed residential consumers.
A1.μsampleμsupplierA2μsampleμsupplierA3 μsample μsupplierA4 μsample μsupplierA5 μsample μsupplierA6 μsample μsupplierA7 μsample μsupplier
19.9823.912188.124.745554531.62535NA9.747105430.29330NA18.86920NA43.44754
13.995 15 35 9 43.2 90 315
58.68 NA 36 45 94.5 NA NA
37.92 30 90 NA 5.8 39.75 180
6.375 15.39 22.5 4 4.98 13.65 4
37.2 42 20.4 NA 21 NA 54
21.8 111 17.5 NA 12.6 0.4 2.4
24.6 4.2 4.14 12 52.5 4.5 84
19.5 135 22.5 22.5 12 31.2 NA
8.55 NA 84 NA NA NA 66
66.78 NA 42.6 NA 27.6 48.6 54
35.07 33 48 2.4 11.07 54 72.9
17.382 83.25 36 6 77.4 NA 21.6
2.34 5.04 1.8 3.6 6 11.52 20.4
3.55 48 15.75 NA 27.6 11.7 NA
0.81 4.5 72 NA 7.2 NA NA
4.43 1.87 4.23 0.9 0.8 NA 72
7.68 27 105 1.68 15.73 1.8 NA
22.08 NA 27 9 6.3 29.9 NA
2.64 8.1 16.56 NA 25.65 NA NA
45.36 3.36 40.05 0.675 69 2.7 1.44
39.51 15 22 NA 69 69.5 12.41
24.708 9 12 3.6 15 6.411 15
83.55 1.62 5.76 2.28 28.5 7.44 46.5
60.33 8.64 5.711 18 6 NA 27.72
57.42 4.07 7.015 8.4 5.25 NA NA
22.43 21.12 35.6 2 23.67 NA 81.9
9.45 13.2 15.25 NA NA NA 5.775
7.05 14.06 2.25 NA 15 13.8 4.8
31.78 15 45.5 16.5 42 1.95 39
2.9 12.54 46.044 4.2 NA 0.6754 NA
7.28 13.11 24.994 0.975 10.83 2.925 NA
16.89 29.4 19.92 15 78.984 45 NA
15.901 0.549 7.05 1.35 5.25 18 9.99
4.95 41.4 21.18 NA 5.25 0.915 42
46.5 4.656 3.7368 24 NA 8.1 NA
24.96 4.035 17.917 NA NA 7.2 4.05
10.440 4.833 15.48 NA NA 59.887 14.16
51.75 31.109 19.98 24 21 2.46 72
23.475 12.44 56.34 4.8 9.75 1.95 NA
13.5 29.4 20.4 NA 7.999 NA NA
25.35 NA 17.76 3 42 NA NA
44.01 9 43.2 2.7 10.5 12.405 5.4
6.405 37.8 30.06 1.407 NA 12.15 7.2
5.4 14.406 22.68 24 NA 8.1 NA
24.6 4.2 184.14 12 54.6 4.5 84
31.08 7.5 17.25 NA 4.8 42 NA
92.58 27 43.92 25.2 26.4 18.9 15
22.62 16.14 18 3 42 32.4 28.35
11.106 56.25 36 7.5 12 12 NA
8.19 50.4 44.33 16.8 22.56 0.1512 0.5376
0 97.05 40.35 NA 235.35 12.6 NA
3 24.99 12.33 6.375 4 NA NA
22.258 23.552 13.41 13.2 46.68 3.15 6.75
7.08 9 25.8 3.6 4.5 10.5 6.93
Table 4. Sociodemographic profile of highest-duration and highest-load electricity consumers.
Table 4. Sociodemographic profile of highest-duration and highest-load electricity consumers.
High-Duration ConsumerHigh-Load Consumer
AreaResidential Unit typeSurface Area (m2)No. of OccupantsIDGenderAgeOccupationEducation LevelMonthly Duration of Individual Consumption (h/Month)IDGenderAgeOccupationEducation LevelMonthly Individual Electricity Consumption (kWh/
Month)		Hourly Specific Consumption (kWh/ h)
ruralhouse1003P1F36–65employedISCED 3843.9P1F36–65employedISCED 395.3190.1129
ruralhouse3763P3M20–35unemployedISCED 7997.5P3M20–35unemployedISCED 7240.1650.2407
ruralhouse3505P2F36–65employedISCED 31158P2F36–65employedISCED 356.1990.0485
ruralhouse6604P2F36–65employedISCED 71469.4P2F36–65employedISCED 7141.7790.0964
urbanflat272P1F20–35studentISCED 7867.69P1F20–35studentISCED 756.7070.0653
urbanflat1504P4F36–65employedISCED 3952.5P4F36–65employedISCED 3232.0050.2435
urbanflat524P2F36–65employedISCED 3999.63P2F36–65employedISCED 3105.4670.1055
ruralhouse1124P3F36–65employedISCED 31097.4P3F36–65employedISCED 3171.0870.1559
urbanhouse804P2M9–19pupilISCED 3978.75P4M36–65employedISCED 7148.548
urbanflat402P1M20–35employedISCED 3405P1M20–35employedISCED 391.50.2259
urbanhouse854P1F36–65employedISCED 32019P1F36–65employedISCED 3209.0050.1035
urbanflat2504P4F36–65employedISCED 3619.5P4F36–65employedISCED 3188.4350.3041
urbanhouse1704P3M36–65employedISCED 31407P1F20–35studentISCED 780.667
urbanflat602P1F20–35studentISCED 6358.5P1F20–35studentISCED 642.5620.1187
urbanflat744P4F20–35studentISCED 71089P2F36–65employedISCED 386.070
urbanflat162P1M20–35studentISCED 3306P2F20–35studentISCED 354.09
urbanflat564P3F36–65employedISCED 21302.87P3F36–65employedISCED 268.8920.0528
ruralhouse1204P2F36–65unemployedISCED 31322.799P2F36–65unemployedISCED 3123.4820.0933
ruralhouse1224P2F36–65unemployedISCED 23453P2F36–65unemployedISCED 2113.7760.0329
ruralhouse1604P2Fover 65retiredISCED 21223.1P2Fover 65retiredISCED 243.7200.0357
ruralhouse1504P3M36–65employedISCED 3627P2F36–65employedISCED 3102.135
urbanflat533P2M20–35employedISCED 71833P2M20–35employedISCED 7291.4370.1589
urbanflat753P2F36–65employedISCED 83303.3P3M36–65employedISCED 861.415
ruralhouse543P2F36–65unemployedISCED 31140P2F36–65unemployedISCED 3136.20.1194
urbanflat501P1M20–35studentISCED 72100P1M20–35studentISCED 7172.1910.0819
ruralhouse902P1F36–65employedISCED 31293P1F36–65employedISCED 375.5540.0584
ruralhouse1003P1F36–65employedISCED 2872.4P1F36–65employedISCED 2139.0690.1594
urbanflat473P1F36–65unemployedISCED 3487.5P1F36–65unemployedISCED 3540.1107
urbanflat362P2M20–35employedISCED 71168.245P1F20–35studentISCED 757.581
ruralhouse702P1F36–65unemployedISCED 33040.5P1F36–65unemployedISCED 3222.5740.0732
urbanflat1403P1F36–65unemployedISCED 31156.8P1F36–65unemployedISCED 360.6190.0524
ruralhouse905P2F36–65unemployedISCED 3758.7P2F36–65unemployedISCED 327.4690.0362
ruralhouse1404P1F36–65employedISCED 71480.98P1F36–65employedISCED 7155.7170.1051
urbanflat552P2F20–35employedISCED 7637.5P2F20–35employedISCED 749.3330.0773
urbanflat563P1F20–35employedISCED 7532.5P3F20–35studentISCED 777.018
ruralhouse1128P3F9–19pupilISCED 3481.5P1F36–65unemployedISCED 747.708
urbanflat422P1M20–35studentISCED 71072.5P1M20–35studentISCED 736.4010.0339
urbanflat534P1M20–35studentISCED 71149.03P3M20–35studentISCED 733.774
urbanhouse1705P4M36–65employedISCED 31177.2P2F20–35unemployedISCED 385.906
ruralhouse3004P1M36–65employedISCED 31689P2F36–65employedISCED 378.998
ruralhouse1304P1F36–65unemployedISCED 21269.579P4F20–35studentISCED 737.909
ruralhouse1805P4F36–65unemployedISCED 31635P4F36–65unemployedISCED 376.8880.0470
ruralhouse2665P2F36–65employedISCED 31242P5Fover 65retiredISCED 346.08
urbanflat1003P3M20–35employedISCED 6645.51P1F20–35employedISCED 353.472
urbanflat473P1M36–65employedISCED 3914.4P2F36–65employedISCED 342.016
ruralhouse654P3F36–65employedISCED 31097.4P3F36–65employedISCED 3171.0870.1559
ruralhouse1464P1M36–65employedISCED 3553.5P4M9–19pupilISCED 362.39
urbanflat1004P2M36–65employedISCED 33258P2M36–65employedISCED 3122.8620.0377
ruralhouse1503P2M36–65employedISCED 71302P1F36–65unemployedISCED 3169.537
ruralhouse1605P2F36–65unemployedISCED 2675P2F36–65unemployedISCED 270.1990.1039
urbanhouse1804P3M36–65employedISCED 31218.06P4F36–65employedISCED 764.434
ruralhouse1505P1F36–65unemployedISCED 61320P1F36–65unemployedISCED 61106.220.8380
urbanflat332P1F20–35studentISCED 71039.98P1F20–35studentISCED 758.1810.0559
urbanflat674P3M20–35studentISCED 71526.4P3M20–35studentISCED 760.0610.0393
urbanflat422P1M20–35employedISCED 62032.8P1M20–35employedISCED 671.7540.0352
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Donciu, C.; Serea, E.; Temneanu, M.C. Residential Electricity Consumption Behaviors in Eastern Romania: A Non-Invasive Survey-Based Assessment of Consumer Patterns. Energies 2025, 18, 4883. https://doi.org/10.3390/en18184883

AMA Style

Donciu C, Serea E, Temneanu MC. Residential Electricity Consumption Behaviors in Eastern Romania: A Non-Invasive Survey-Based Assessment of Consumer Patterns. Energies. 2025; 18(18):4883. https://doi.org/10.3390/en18184883

Chicago/Turabian Style

Donciu, Codrin, Elena Serea, and Marinel Costel Temneanu. 2025. "Residential Electricity Consumption Behaviors in Eastern Romania: A Non-Invasive Survey-Based Assessment of Consumer Patterns" Energies 18, no. 18: 4883. https://doi.org/10.3390/en18184883

APA Style

Donciu, C., Serea, E., & Temneanu, M. C. (2025). Residential Electricity Consumption Behaviors in Eastern Romania: A Non-Invasive Survey-Based Assessment of Consumer Patterns. Energies, 18(18), 4883. https://doi.org/10.3390/en18184883

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