Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the primary cause of the pandemic that started in 2019. COVID-19 has significantly influenced life all over the world. It spread rapidly, infected around half a million people, and killed more than 20 thousand over the globe until 27 March 2020 (https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200327-sitrep-67-covid-19.pdf). The impact of COVID-19 was devastating worldwide, in terms of the economy, health, social and emotional aspects. An unprecedented nation-wide lockdown brought a grinding halt to economic activities, adversely affecting the lives and livelihoods of thousands of daily wage workers. The economic impact was drastic, which led researchers to study transformation in every sector.
The first positive case of COVID-19 was reported in India on 27 January 2020 (Andrews et al. 2020). By mid-April 2020, India recorded around 12,370 COVID-19 cases and 423 deaths (https://www.worldometers.info/coronavirus/country/india/). Initially, many steps were taken to control the spread. The prime minister of India called for ‘Janata curfew’ on 22 March from 7 AM-9 PM, urging people to stay home except those working in essential services and enforcing public led social distancing interventions (https://www.who.int/india/emergencies/coronavirusdisease-(covid-19)/india-situation-report). However, the nation was still not able to completely control the situation. Finally, on 25 March 2020 the first lockdown for the entire nation was imposed to tackle the spread of the pandemic (https://www.timesnownews.com/india/article/march-25-2020-the-dayindia-went-into-nationwide-lockdown-to-tackle-coronavirus/736838). The focus was on the closure of all activities except essential services such as hospitals, telecom, pharmaceuticals, and provisional stores (https://www.mha.gov.in/sites/default/files/Guidelines_0.pdf). People saw technology and internet-based services as a major source to communicate, interact, and perform their work from home (https://zeenews.india.com/technology/how-technology-changed-lives-during-covid19-lockdown-2349849.html).
Above all, these restrictions affected production activities and individual ways of living, leading to noticeable changes in energy consumption. According to the reports, more than 80% of the workspaces were closed completely or partially worldwide (Chen et al. 2020). Globally, the energy demand reduced by 3.8% within the first three months of lockdown (https://www.iea.org/reports/global-energy-review2020), although there was an increase in the energy demand for residential buildings (https://www.tdworld.com/distributed-energy-resources/demand-sidemanagement/article/21128542/covid19-is-changing-residential-electricity-demand). UK, Spain, France, and India saw their consumption decreased by almost 15% during lockdown periods. The electricity demand of Italy also decreased by 35%. In China, energy utilization dropped by 6.5% in the first quarter and (https://www.iea.org/reports/covid-19-impacton-electricity). Studies (https://www.iea.org/reports/covid-19-impacton-electricity; Ashkanani et al. 2022; https://www.iea.org/reports/portugal-2021) have shown that overall country level consumption decreased during the pandemic. A case study done on Turkey by Yukesltan et al. (2022), using a modulated Fourier series expansion on overall electricity consumption, forecasted that the demand will decrease by up to 12% according to the level of restrictions imposed. Furthermore, Wen et al. (2022) predicted a decline of 12% electricity demand for New Zealand using the auto-regressive-moving-average model. Sánchez-Úbeda et al. (2022) analysed electricity demand of Latin America and Caribbean countries (Peru, Bolivia, Costa Rica, Brazil, Guatemala, Mexico, Dominican Republic, Argentina, Chile, and Uruguay), and found a decrease of 30% for Peru and Bolivia. While a decrease of 6% was observed for Chile and Uruguay, remaining countries had a reduction by 11 to 17%. Alavi et al. (2022) observed a reduction of electricity consumption for Bangladesh by 50%. In addition, they developed a neural- network based prediction model, which can predict electricity consumption for Bangladesh if a lockdown is announced in future.
Several studies (Edomah and Ndulue 2020; Burleyson et al. 2021) have shown that there was an energy shift from commercial and industrial buildings to residential buildings. Sanchez-lopez et al. (2022) conducted a similar kind of study on the impact of electricity demand in Chile, using data obtained from 230 thousand smart meters. They noticed a rise of 17% in June, when compared to the same month of 2019. Conversely, the industrial electricity demand was reduced by 75%. Krarti and Aldubyan (2021), through their review study, observed the post-pandemic effect on energy consumption of the residential sector as a function of normalized weather time series data. A rise of 11–32% for few countries (Australia, UK, and the USA) during complete lockdown was observed. This massive shift of energy demand from industrial and commercial buildings towards residential buildings provides an idea of the impact of lockdown during the pandemic.
Several researchers have studied the impact of COVID-19 on residential energy demand. A case study done by Aldubayan and Krarti (2022) on residential buildings of Saudi Arabia evaluates the short-term impact of the lockdown. Their study finds a surge of 25.2% in electricity consumption during the lockdown period. Later, when residential building stock models were used, using normalised weather conditions, they found an increase of 16% in housing energy compared to the year 2019. Most of the increase was due to a significantly higher usage of lighting, appliances, and air-conditioning associated with increased occupancy levels during daytime hours. Further, using the same validated residential building model they forecasted that the overall energy consumption can increase upto 13.5% if stay-home living continues compared to the year 2019. Utilizing the information gathered from a study in Ireland, the general increase in energy utilization for houses has been assessed to range between 11 and 20% during the lockdown time frame, with even higher increments happening during 9 AM to 5 PM on working days (https://www.savills.us/insight-and-opinion/savills-news/299070/covid-19-restrictions-changing-the-daily-patterns-of-energy-consumption).
Meanwhile, Abdeen et al. (2021) explored the household hourly electricity consumption in Canada. Using measured electricity consumption data from 500 homes in Ottawa, they observed that daily electricity consumption increased by 12% relative to the non-COVID year. A similar kind of study done by Qarnain et al. (2020) found 34 factors responsible for driving more energy consumption in the residential sector of India, finally concluding that energy was consumed more intensely in pandemic times than in pre-pandemic times. An increase of 12% residential energy consumption after a few weeks of lockdown was reported by Austin Energy (https://www.statesman.com/story/news/local/flashbriefing/2020/03/25/austins-coronavirus-stay-home-order-could-swell-utilitybills/1459345007/). Besides, evaluating the high- frequency electricity data from 491 houses and interviews on household energy consumption with 17 families in Queensland, Snow et al. (2020) compared energy use before and during COVID-19 lockdown. They estimated the key factors responsible for household electricity consumption, and cooking and digital devices were the major contributors. Rouleau and Gosselin (2021) recently studied a 40-dwelling social housing building located in Canada and noticed occupants were using more electricity from 9:00 AM to 5:00 PM. An increase of 46% was seen relative to the same month in pre-COVID time. A study done by Bielecki et al. (2021) on data obtained from 7000 flats, observed that while energy consumption has increased, there was no change in the average daily peak of these houses. Simulation studies were conducted by few researchers (Li et al. 2021; Ding et al. 2021; Ku et al. 2022) to estimate the long-term impact of the lockdown on residential electrical demand. They estimated an increase in the electricity consumption range of 13–27%.
There were also few studies conducted on the variation in energy patterns and the increase of peak demand for residential buildings. The studies also tried to find which devices contributed to the increase in energy consumption. Aldubyan and Krarti (2022) further demonstrated the hike of peak demand by 15 to 20% in post-COVID as compared to pre-COVID. Li et al. (2021) through their simulation results also predicted a hike of 35–53% in residential hourly peak demand between 12 and 5PM. An energy use survey was conducted by Huebner et al. (2021) in the UK for 1016 participants during the first lockdown in March 2020. The survey data concluded that the electricity demand was more during the day and Italy showed an increased usage of cooking appliances, television and computers. Surahman et al. (2022) investigated household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic. Statistical analysis performed on the survey results received from 311 residents concluded that the average annual energy consumption of samples taken was larger by 3 GJ during pandemic. The increase was majorly due to excessive use of AC and cooking appliances. A study done by Chinthavali et al. (2022) noticed a significant change in the pattern of electricity load in residential buildings during weekdays in lockdown. They also confirmed that HVAC and water heaters are the largest consumers of electricity in residential homes. Further, Kranti and Aldubyan (2021) added that most of the energy was consumed by HVAC. Kawka and Cetin (2021) compared the HVAC loads, non-HVAC loads and overall loads of 225 housing units located in Texas, for the duration 2018–2020 and concluded that maximum energy consumption in non-HVAC residential buildings occurred between 10 AM-5 PM while HVAC loads also increased for the lockdown period.
According to data published by Times of India (TOI), India also witnessed a decline in energy consumption of 25% in the last week of March 2020, which was more than the decline that occurred in the US and Europe (https://www.indiatoday.in/india/story/india-s-decline-in-electricityconsumption-due-to-lockdown-more-severe-than-us-and-eu-1666444-2020-04-13). A study done by the Prayas (energy group) (https://www.prayaspune.org/peg/blogs/household-electricity-consumption-in-indiaduring-the-covid-19-lockdown-insights-from-metering-data.html) on minute-wise load and voltage data of 81 households located in Uttar Pradesh and Maharashtra from 4 March to 5 May 2020, observed that the daily average household energy consumption increased by 26% in the lockdown period as compared to the pre-lockdown period. The finding was that among all household equipment, Heating, Ventilation, and Air Conditioning (HVAC) and water heaters consumed the most energy. They analysed houses with and without AC and concluded that AC-homes consumption increased by 45–60% while non-AC homes energy consumption increased by 22% as compared to pre-COVID times. In their study, they showed that residential energy demand depends upon the size of the home, occupancy, climate of the place, and the location. Apart from this small-scale analysis there is a lack of similar studies in the Indian context.
The objective of our study is to evaluate the impact of COVID-19 on the AC energy and household electricity (excluding AC energy) consumption independently. AC energy indicates the amount of energy consumed by AC and household electricity indicates the total energy consumed by remaining electrical appliances excluding AC. Further, the following hypotheses were made, and tested:
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Working from home during weekdays would lead to an increase in energy consumption since occupants may cook lunch, use AC and other electrical devices during the day.
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The energy consumption during night-time is expected to reduce due to the pre-cooling effect when ACs are operated during daytime.
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There may be a delay in the morning peak since people may wake up late as they don’t need to commute due to the work from home.
The results were also checked to confirm whether these points are statistically significant or not. The paper is structured into five sections: “Methodology” section provides the details of study area and selection of month for the analysis, followed by details of the buildings selected for the data collection and data pre-processing. “Results” section provides the results obtained from the analysis, comparison between non-COVID and COVID year for the overall AC energy consumption and household electricity consumption of the homes. The operating hours for the AC were also compared for both the years. Further insights are provided for the daytime and nighttime AC energy consumption, operational hours, and household electricity consumption for the same, followed by statistical tests for significance and frequency distribution of the data. A comparison with the current literature was done in “Discussion” section. Finally, the paper is concluded in the last section along with the test results.