Minseok Jang’s research while affiliated with Korea University and other places


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Publications (5)


Analysis of Residential Consumers’ Attitudes Toward Electricity Tariff and Preferences for Time-of-Use Tariff in Korea
  • Article
  • Full-text available

January 2022

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47 Reads

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10 Citations

IEEE Access

Minseok Jang

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Hyun Cheol Jeong

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Taegon Kim

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[...]

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Sung-Kwan Joo

In recent years, a Time-of-Use (TOU) tariff for residential consumers has received much more attention with the increasing deployment of smart meters for residential consumers in Korea. The introduction of the TOU tariff for residential consumers is expected to allow residential consumers to have a choice of electricity rate plans in addition to load management. An analysis of residential consumers’ preferences for the TOU tariff is needed to identify TOU attributes’ levels. This paper analyzes Korean consumers’ attitudes toward current residential electricity tariff and preferences for TOU tariff using demographic characteristics collected by a face-to-face survey. Consumers’ preferences on key attributes of TOU tariff are analyzed using the conjoint analysis, and attitudes toward current residential electricity tariff are estimated by the multiple indicators multiple causes (MIMIC) model. The analysis results of this study show that based on their attitude toward electricity tariff, consumers’ attitudes can be divided into three latent variables, and preferences for TOU tariff are discrete by group rather than continuous by individual consumer.

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Figure 6. Change in load shape groups' household hourly energy consumption due to increase in the time-varying reproduction number of COVID-19.
Estimated Reproduction Number During the Period March to April 2020 in Korea [18].
Summary of time variant variable statistics.
Summary of demographic characteristic.
Models to estimate the impact of COVID-19 on household energy consumption.
Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape

November 2021

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189 Reads

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13 Citations

Energies

Since January 2020, the COVID-19 pandemic has been impacting various aspects of people’s daily lives and the economy. The first case of COVID-19 in South Korea was identified on 20 January 2020. The Korean government implemented the first social distancing measures in the first week of March 2020. As a result, energy consumption in the industrial, commercial and educational sectors decreased. On the other hand, residential energy consumption increased as telecommuting work and remote online classes were encouraged. However, the impact of social distancing on residential energy consumption in Korea has not been systematically analyzed. This study attempts to analyze the impact of social distancing implemented as a result of COVID-19 on residential energy consumption with time-varying reproduction numbers of COVID-19. A two-way fixed effect model and demographic characteristics are used to account for the heterogeneity. The changes in household energy consumption by load shape group are also analyzed with the household energy consumption model. There some are key results of COVID-19 impact on household energy consumption. Based on the hourly smart meter data, an average increase of 0.3% in the hourly average energy consumption is caused by a unit increase in the time-varying reproduction number of COVID-19. For each income, mid-income groups show less impact on energy consumption compared to both low-income and high-income groups. In each family member, as the number of family members increases, the change in electricity consumption affected by social distancing tends to decrease. For area groups, large area consumers increase household energy consumption more than other area groups. Lastly, The COVID-19 impact on each load shape is influenced by their energy consumption patterns.


Short-Term Residential Load Forecasting Using 2-Step SARIMAX

October 2021

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25 Reads

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5 Citations

Journal of Electrical Engineering and Technology

In contrast to city-level and larger aggregate-level load forecasting, load forecasting for residential customers is a much more challenging problem because residential loads are much more volatile. In order to forecast the residential load at one-hour interval 24-h loads the day before, a 2-Step SARIMAX method for residential load forecasting is proposed in this study. The forecasting performance of the proposed method is compared with the existing forecasting methods including SARIMA.


Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

September 2021

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56 Reads

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18 Citations

Energies

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.


Clustering of Load Profiles of Residential Customers Using Extreme Points and Demographic Characteristics

January 2021

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410 Reads

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17 Citations

Electronics

In this paper, a systematic method is proposed to cluster the energy consumption patterns of residential customers by utilizing extreme points and demographic characteristics. The extreme points of the energy consumption pattern enable effective clustering of residential customers. Additionally, demographic characteristics can be used to determine an effective extreme point for the clustering algorithm. The K-means-based features selection method is used to classify energy consumption patterns of residential customers into six types. Furthermore, the type of energy consumption pattern can be identified depending on the characteristics of residential customers. The analytical results of this paper show that the extreme points are effective in clustering the energy consumption patterns of residential customers.

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Citations (5)


... Reference [11] introduces a coupon incentive-based DR program is introduced, and the authors evaluate benefits of the DR program in terms of social welfare, consumer surplus, and robustness of retail electricity prices. The authors of [12] analyze the attribution of customers towards electricity price and customers' preferences. Reference [13] provides the rebate levels calculated with the aim of maximizing the retailers' profits when price spikes are detected. ...

Reference:

Theoretical study on demand-side management to reduce imbalance between electricity supply and demand
Analysis of Residential Consumers’ Attitudes Toward Electricity Tariff and Preferences for Time-of-Use Tariff in Korea

IEEE Access

... Szenario 2: Anzeigemöglichkeit für Verbraucher*innen plus Kostensparhinweise. 4 Im Scoping Review auch bei:Beckel et al. 2014;Gholami et al. 2021;Viegas et al. 2016 5 Im Scoping Review auch bei:Anderson et al. 2017;Beckel et al. 2014;Besagni et al. 2020;Gholami et al. 2021;Jang et al. 2021;Tang et al. 2022 6 (Kowalska-Pyzalska et al. 2020 ...

Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape

Energies

... In preceding years, numerous models for forecasting electricity demand have leaned on methods, such as ARIMA, Seasonal Autoregressive Integrated Moving Average (SARIMA), and SARIMAX (SARIMA with exogenous variables) [15,16]. These models, designed for time-dependent data, are particularly effective in predicting electricity demand in scenarios where the required forecasting series is linear in nature. ...

Short-Term Residential Load Forecasting Using 2-Step SARIMAX
  • Citing Article
  • October 2021

Journal of Electrical Engineering and Technology

... Africa Australia overall income, household type, employment status, etc. Table 2 presents these themes, the papers that address them, and the methods used. Segmentation of consumers and distributional effects [12], [13], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34] • Willingness-to-adopt [14], [29], [35], [36], [37], [22], [38], [39], [40], [41], [42], [43], [44], [45], [46] • Closing the intentionaction gap [22], [23], [20], [29], [37], [44], [47], [30], [48], [49], [50], [51], [52], [53], [54], [55] • [15], [22], [44], [34], [56], [57], [58], [59], [60] • Survey and interviews • Permutation equivariance-enabled attention mechanism • Agent-based modelling/simulation • Field experiment and/or statistical analysis on historical consumption data • Utility functions of households to study subsistence during different hours ...

Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

Energies

... To ensure that these scenarios reflect the whole community correctly, we apply stratified sampling of consumer profiles from the larger pool. A widely used method for identifying these strata in energy demand modelling is clustering on energy demand profiles [36,37]. In this study, we use K-means clustering on daily averaged consumption in the winter months of the prosumers. ...

Clustering of Load Profiles of Residential Customers Using Extreme Points and Demographic Characteristics

Electronics