Monthly changes in energy consumption patterns before and after COVID-19 in low-income housing (public housing). Changes in Residential Complex Pre- and Post-Covid-19 Daytime Residential Patterns in Low-Income Housing (Public Housing). Monthly changes in energy consumption patterns before and after COVID-19 in high-income housing (high-price housing).
Changes in residence patterns of the population in an apartment complex during the day before and after COVID-19 in high-income housing (high-priced housing). Patterns of residence of residents who stay in the apartment complex during the day on weekends and weekdays. a) low-income housing (public housing); (b) High-income housing (high-priced housing).
INTRODUCTION
For example, Kang et al. 2021) studied the difference in energy consumption patterns according to building type and the number of COVID-19 confirmed cases. However, changes in energy consumption patterns in the COVID-19 pandemic situation have mainly been studied from a global perspective, and studies conducted from a local perspective are limited (Zhang et al., 2020). This study sought to study how COVID-19 affected residential energy from a local perspective by analyzing changes in energy consumption in apartment complexes.
LITERATURE REVIEW
Yun & Steemers, 2011), however, it may vary depending on the window or retrofit measurement (Choi et al., 2014) or indoor applications. Since demographic characteristics show different impact through their income level or their household composition (Brounen et al., 2012; van den Brom et al., 2018), the effect on energy consumption according to age group different results were observed for each study. Besagni and Borgarello (2018) showed that the energy consumption per household increases as the number of children increases.
But van den Brom et al. 2018) showed a different result depending on the level of income or the number of parents. Middle- or high-income groups tend to save less energy but are more likely to invest in energy-efficient retrofitting measures (Trotta, 2018), while low-income groups tend to use less energy (Besagni & Borgarello, 2018). Finally, it is widely known that outdoor weather, especially temperature, and green areas such as a park, water body or other open space have a significant impact on energy consumption through evaporation, heat absorption. Access to essential services (for example, heating, cooling, cooking, lighting, and medical devices) is strongly related to energy consumption (Laldjebaev & Sovacool, 2015) and is also directly related to health, including physical and mental health (Zhang et al. , 2021), but energy insecurity during COVID-19 has not yet been fully explored.
Being unable to meet a household's essential energy needs is defined as energy insecurity (Lou et al., 2021). Furthermore, due to their time scale limitations, these studies did not adequately consider seasonal changes in energy expenditure patterns and could not identify the multidimensional effect, which varied depending on socio-economic and demographic factors (Collivignarelli et al., 2020). The marginal communities have limited opportunities to access decent work (Blustein et al., 2019) and experienced a greater risk of
Many researchers warn that new infectious diseases after COVID-19 may threaten the global community again (Elengoe, 2020; Sachs et al., 2022).
COVID-19 IN SOUTH KOREA
According to statistics on energy consumption in buildings released by the Ministry of Land, Infrastructure and Transport of Korea (MOLIT), the total energy consumption in 2020 when the outbreak of COVID-19 was 33 million TOE, which is 1.1 % less than the previous year and increased. again by 3% to 34 million TOE in 2021. In particular, in the second quarter of 2020, immediately after COVID-19, energy consumption of residential buildings increased by 2.4%, while non-residential buildings decreased by 11.6%. From this, it can be concluded that energy consumption in apartments has increased, as people spend more time at home due to COVID-19.
Residential energy also showed different patterns for each type of building, with detached houses decreasing by 0.6% in 2020 and apartments increasing by 3.1%. Thus, different patterns appear depending on the purpose and type of building and efforts to understand energy.
MATERIALS & METHODS
- Study Area
- Study Samples
- Spatial Join
- Data preprocessing
- Statistical analysis
Monthly building energy consumption for each apartment complex needs to be collected to analyze differences due to the COVID-19 pandemic and government measures such as social distancing levels. Data on monthly energy consumption (i.e. electricity consumption, kWh) and basic information of the relevant residential complex were collected by the API service of the k-apt system. In this study, energy consumption per total floor area and energy consumption per living population were compared and analyzed during the COVID-19 period.
In this study, the main subject analyzed were monthly electricity consumption and live population data. In this study, total floor area means the sum of the total exclusive private area of all residential buildings included in the apartment complex. The sum of the total exclusive private area of all residential buildings included in the family apartment complex Number of families in the apartment complex family size Average floor area per family of the apartment.
Therefore, it is necessary to adjust the space unit according to apartment complex unit or join the corresponding value. Besides energy consumption data (i.e., monthly electricity consumption data of each apartment complex) and basic information of apartment complex, other data had different spatial scale. Also in this process, some apartment complexes whose area does not match the output area scale were excluded.
Also, to collect temperature data, each housing complex was paired with the nearest AWS point.
RESULTS
- Descriptive statistics
- Energy-related behavior pattern changes by income-level after COVID-19
- Apartment electricity consumption per total floor area
- Apartment electricity consumption per living population
However, as COVID-19 has persisted and entered its late stages (i.e. May 2021 to December 2021), energy consumption per total floor area has increased. Similar to the energy consumption pattern, the living population gradually increased to 1.6% (from 825 to 838) and 2.4% (from 841 to 861) in the early and middle phases of the COVID-19 crisis, respectively. sharply to 7.2% (increase from 843 to 904) in the late phase compared to the same period before the COVID-19. Monthly energy consumption and monthly energy consumption per unit area showed different patterns in low-income apartments.
Changes in energy consumption/living population patterns before and after COVID-19 in the low-income apartment (public apartment). During the period of COVID-19, energy consumption per living population of the high income class increased by 1.8%. In low-income apartments, energy consumption per living population has decreased, but in high-income apartments, on the contrary, it has increased except for the month of August.
Changes in energy consumption/living population patterns before and after COVID-19 in the high-income apartment (expensive apartment). Total energy consumption and energy consumption per total floor area will increase significantly towards the end of COVID-19. However, energy consumption per living population increased most in the early stages of COVID-19.
The results of multivariate regression analysis are performed with two energy consumption units, energy consumption per total floor area and electricity consumption per living population. The effects of income level and COVID-19 also showed different patterns from energy consumption per total floor area. Similarly, no significant results were produced in high-income apartments by COVID-19 period, but a negative correlation between mid-, late-stage of COVID-19 and energy consumption per living population was confirmed in low-income apartments.
DISCUSSION
The living population data used in this study are estimated data and have the limitation of making validation difficult. However, the live population data used in this study are estimated data and have the limitation of being difficult to verify validation. However, the fact that South Korea has the largest number of smartphone owners in the world (Silver, 2019) and that KT, which collected mobile phone signals, also has the second largest share, is a testament to the reliability of the data.
Nevertheless, the population of the apartment building can be collected based on the spatial scale of the apartment building. However, there is a limitation that it is difficult to check whether it is outdoor or indoor. In addition, because the apartment buildings are concentrated high-rise buildings as a group, the microclimate can be influenced by the structure of the apartment building, such as density, open space and shade, and can cause a change in energy consumption. Another point is that no different result was observed between low-income and high-income apartments in multivariate regression analysis of energy consumption per total floor area.
This could be due to energy consumption in high-income apartments and energy inefficiency in low-income apartments, but it could also be because there was a small difference between middle-income and low-income apartments. Because many apartment complexes were used in this study, low-income apartments may have been included in the middle-income apartments when classifying middle- and high-income apartments based on the real transaction price of apartments.
CONCLUSION
The impact of the COVID-19 pandemic on US electricity demand and supply: An early view from the data. Revealing the Inequitable Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Energy Use Characteristics vs. Energy Use Characteristics and Evaluation of Thermal Insulation Performance According to Year of Housing Construction.
Does energy subsidy affect indoor temperature and heating energy consumption in low-income households. A Structural Equation Analysis of the Effects of Household and Building Characteristics on Annual Energy Use in US Residential Buildings. A structural equation model of energy use in the United States: Exploring the complexity of energy use per inhabitant.
Changes in energy consumption by building use type under the COVID-19 pandemic in South Korea. COVID-19 and overall mortality disparities in increased death rates by zip code characteristics: Massachusetts, January 1 to May 19, 2020. The effect of physical characteristics of a dwelling on energy consumption and emissions: The case of Castellón and Valencia (Spain).
When pandemics impact economies and climate change: Exploring the impact of COVID-19 on oil and electricity demand in China.
