ABSTRACT
In this paper, using data from Romania, we analysed the changes in electricity consumption generated during the COVID-19 crisis, and the measures taken against the spread of the coronavirus to limit the effects of the pandemic. Using a seasonal autoregressive econometric model, we found that, beyond seasonal (weekly, monthly, quarterly, yearly) effects, the average daily electricity real consumption in Romania, during the state of the emergency period (16 March 16 to 14 May 2020) decreased by -194.8 MW (about -2.9%), compared to the historical data (2006-March 2022), and this decrease is not due to the action of some random factors, and it is not a manifestation of domain-specific seasonality. The literature discusses the hypothesis that during the pandemic time, the profile of daily electricity consumption on weekdays was close to the typical Sunday profile. We tested a similar hypothesis for Romania. As a methodology, we tried to go beyond the simple interpretation of statistics and graphics (as found in most papers) and we calculated some measures of distances (the Mahalanobis distance, Manhattan distance) and similarity (coefficient of correlation, cosines coefficient) between the vectors of daily electricity real consumptions, by hourly intervals. As the time interval, we have analysed, for Romania, the electricity real consumption over the period January 2006-March 2022, by day of the week and within the day, by hourly intervals (5911 observations). We found (not very strong) evidence supporting a hypothesis that, in the pandemic crisis, the profile of electricity consumption approaches the weekend pattern only for the state of the emergency period, and we could not find the same evidence for the state of the alert period (June 2020-March 2022). The strongest closeness is to the hourly consumption pattern of Saturday. That is, for Romania, in terms of electricity consumption, "under lockdown, every day is a Sunday" (Staffell) it is rather "under lockdown, every day is (almost) a Saturday"! During the state of the alert period, consumption returned to the pre-crisis profile. Since certain behaviours generated by the pandemic have been maintained in the medium and long term (distance learning, working from home, online sales, etc.), such studies can have policy implications, especially for setting energy policy measures (e.g., in balancing load peaks).
ABSTRACT
The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.
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The residential sector in Thailand has been a fast-growing energy consumption sector since 1995 at a rate of 6% per year. This sector makes a significant contribution to Thailand's rising electricity demand especially during the COVID-19 pandemic. This study projects Thailand's residential electricity consumption characteristics and the factors affecting the growth of electricity consumption using a system dynamics (SD) modeling approach to forecast long-term electricity consumption in Thailand. Furthermore, the COVID-19 pandemic and the lockdown can be seen as a forced social experiment, with the findings demonstrating how to use resources under particular circumstances. Four key factors affecting the electricity demand used in the SD model development include (1) work and study from home, (2) socio-demographic, (3) temperature changing, and (4) rise of GDP. Secondary and primary data, through questionnaire survey method, were used as data input for the model. The simulation results reveal that changing behavior on higher-wattage appliances has huge impacts on overall electricity consumption. The pressure to work and study at home contributes to rises of electricity consumption in the residential sector during and after COVID-19 pandemic. The government and related agencies may use the study results to plan for the electricity supply in the long term. © 2023 World Scientific Publishing Co.
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This paper establishes electricity consumption as an indicator for tracking economic fluctuations in Bangladesh. It presents monthly data on national electricity consumption since 1993 and subnational daily consumption data since February 2010. Electricity consumption is strongly correlated with other high-frequency indicators of economic activity, and it has declined during natural disasters and the COVID-19 lockdowns. The paper estimates an electricity consumption model that explains over 90% of the variation in daily consumption based on a quadratic trend, seasonality, within-week variation, national holidays, Ramadan, and temperature. Deviations from the model prediction can act as an indicator of subnational economic fluctuations. For example, electricity consumption in Dhaka fell around 40% below normal in April and May 2020 during the first COVID-19 lockdown and remained below normal afterwards. The later lockdowns, in contrast, had much smaller impacts, in line with less stringent containment measures and more effective adaptation.
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Decarbonization, decentralization, and digitalization are the prominent paths for the energy sector in the future. The rise of smart meters across consumers, and industries led to a massive collection of fine-grained energy and electricity consumption-related data. A data science challenge is to analyze the Smart Meter data for the benefit of both the energy providers and the consumers. In this paper, An attempt has been made to analyze the smart meter collected from the IIT Hyderabad campus and presented the analysis into descriptive, predictive, and prescriptive analytics. The data collected from more than 50 meters over a period of one year have been analyzed and results obtained. Interesting trends such as the impact of COVID-19 on campus energy consumption have been examined. The framework for energy data analytics presented in this paper will be useful for any campus in general, and the recommendations presented will save energy expenses. © 2023 IEEE.
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As a consequence of the COVID-19 pandemic, some patterns of energy consumption changed in the residential and non-residential sectors. This paper uses data from a local utility company in Florida to quantify the heterogeneous impacts of the pandemic on electricity and natural gas consumption across households from different income levels and across essential and non-essential businesses. We found significant increases in the average residential electricity consumption during the lockdown and subsequent reopening phases, which translate into higher cost for households. We found that natural gas consumption dropped abruptly in the business sector and also important differences between the electricity consumption of essential and non-essential businesses, with the former consuming more and the latter less electricity. © 2023 by the IAEE. All rights reserved.
ABSTRACT
Le débat relatif à l'impact environnemental du numérique présente un degré de complexité qui ne peut être approché à la seule observation de la progression de son poids dans les émissions de CO2 ou les consommations électriques. Des travaux récents permettent de mieux appréhender ses effets induits, en établissant notamment dans quel sens les usages du numérique influencent la trajectoire des émissions des États ou agissent sur des cobénéfices de l'action climatique (comme la qualité de l'air). En outre, ces analyses devront être resituées dans le prolongement de la crise sanitaire (et du développement des activités socio-économiques « à distance »), ainsi que dans celui de la crise énergétique (qui implique une optimisation de systèmes gagnant en complexité du fait d'un développement accéléré des renouvelables, des efforts d'efficacité...). Ces travaux débouchent sur un constat contrasté de l'impact environnemental du numérique (qui, toutefois, n'invalide pas l'impératif de l'effort de sobriété).Alternate :The debate on the environmental impact of digital technology is complex and cannot be approached simply by observing the increase in its weight in CO2 emissions or electricity consumption. Recent work has made it possible to improve the understanding of induced effects, in particular by establishing the extent to which the uses of digital technology influence the emissions trajectory of States or act on the co-benefits of climate action (such as air quality). Furthermore, these analyses must be placed in the context of the health crisis (and the development of 'remote' socio-economic activities), as well as the energy crisis (which involves optimising systems that are becoming increasingly complex due to the accelerated development of renewables, efficiency efforts, etc.). This work leads to a contrasting assessment of the environmental impact of digital technology (which, however, does not invalidate the need for sobriety efforts).
ABSTRACT
Extreme disruptive scenarios such as pandemic lockdown force people to alter regular daily routines, impacting their energy consumption pattern. The implication of such a disruptive scenario for a more extended period on energy consumption is uncertain. This study aimed to investigate the impact of COVID-19 lockdown on residential electricity consumption in 100 houses from the southwestern UK. For the study, we analysed highly granular (1-minutely) electricity consumption data for April-September 2020 compared to the same months in 2019 for the same houses. Our study showed statistically significant differences during the lockdown period (the analysed six months) in energy demand. The minutely average electricity demand was 1.4-10% lower during April-September 2020 than in 2019. Our analysis showed that not all houses had similar type of changes during the lockdown. Some houses demonstrated a 38% increase in electricity demand, whereas some houses showed a 54% reduction during the lockdown period compared to 2019. Some houses showed significantly higher electricity use during the morning and afternoon than in 2019, which might be due to working and schooling from homes during the lockdown. © International Building Performance Simulation Association, 2022
ABSTRACT
Many studies conducted previously have reported that due to lockdowns or stay-at-home orders associated with the COVID-19 pandemic in April 2020 residential power consumption has increased in countries, particularly in cities worldwide. This study compared the power consumption of 1,339 detached houses in Japan over the past three years as well as a year after the pandemic and analyzed living behavioral changes in the 12 months after the pandemic using a questionnaire survey of occupants. As of March 2021, which is after 12 months of the beginning of the pandemic, it was confirmed that the way of life had returned to almost normal, and as a factor in increasing consumption, working from home would remain the only behavioral change that may take root in Japanese society.
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This study attempts to study the impact of social and economic constraints, identification of new diseases, wind and solar energy consumption during the 2019 crisis on daily electricity demand by constructing multivariate correlation regression. The aim of the study is to determine the impact of the COVID-19 pandemic on the structure of electricity consumption by building regression models to analyse how various variables (detection of new diseases, wind and solar energy consumption) and social behaviour affect electricity demand. Tasks: to identify the main dates from the chronologies of COVID-19 in Russia, compare the electricity indicators by years, compare the data with the pre-pandemic period, study the share of generated electricity in the balance, conduct a correlation-regression analysis in order to identify the relationship between the detection of new cases of COVID-19 disease in the period from 03/30/2020 to 10/27/2021 and energy consumption, to study the impact of social activity on the level of consumption of renewable energy sources. This study identified links between new cases of coronavirus disease and energy consumption;wind energy consumption and general indicator;consumption of wind energy and solar with an indicator of morbidity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This paper uses social electricity consumption data from 2015-2021 in a city in Hubei province, and uses some methods of artificial intelligence, for example, python function fitting and machine learning to construct an impact analysis and prediction model of the COVID-19 epidemic on Electricity Consumption. Through comparison with the effects of general linear regression and polynomial regression, a better model is developed which comprises four independent variables and uses polynomial regression. The model developed in this paper helps to quantify and measure the impact of the epidemic on society's electricity consumption, and ultimately enables users in the electricity industry to make convenient and rapid forecasts, helping them to make reasonable power supply plans, trading plans and dispatch plans, and to ensure safe and economic operation of the Electricity System. © 2022 ACM.
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The COVID-19 pandemic had a noticeable effect on household energy consumption. In addition, modern architecture has driven growth in Indonesia's property sector in recent years and is one of the biggest energy consumers. The COVID-19 pandemic along with modern lifestyles like using advanced residential appliances have contributed to increased energy consumption in Indonesia. Homeowners do notice an impact on their electricity usage from a large-scale social restriction policy (PSBB). Predicting appliance future utilization and optimizing space are key to the energy management of residential buildings. Data collected from 150 households in Sumatra and Java Island, Indonesia, were used to compare three different house designs. The purpose of this study is to determine whether household lifestyle influences residential energy consumption. According to the analysis, household electricity consumption increased by around 11% between 2020 and 2019. eQUEST simulation analysis reveals that roof design has a small impact on reducing energy consumption. In three urban centers in Indonesia: Batam, Semarang, and Jakarta, it did not show a significant reduction in electricity consumption. The largest contributor to energy consumption patterns is household habits. The use of miscellaneous equipment (laptop, handphone, water pump, washing machine) and the use of air conditioners have significant effects on energy choice behavior, emphasizing the importance of building planning. Changing electricity usage behavior and water-saving management can lead to achieving energy efficiency targets in residential buildings. © 2023 by authors, all rights reserved.
ABSTRACT
Pakistan is in a terrifying and devastating energy crisis. Recently, the prediction for energy consumption has intensified compared to its production capacity, which is problematic for Pakistan's social and economic stability. Hence, it is vital to examine the link between power consumption, power prices, urban transition, other electricity use, and economic expansion from 1970 to 2018 in Pakistan. For analysis, the second-generation econometric technique of Lee and Strazicich (2013), novel Augmented Autoregressive Distributed Lag (AARDL), and Frequency Domain Causality (FDC) is useful to detect the long-medium and short-run association among the variables. The results show that power consumption stimulates economic expansion in the short and long-run, though the rise in power prices declines economic activity in the short and long-run. Also, urban transition and other electricity use are a substantial positive and negative impact on economic expansion in the short and long-run. The outcome suggests that efficient energy supply, low-cost power prices, proper urban transition management, and other energy use could be useful for policymakers to achieve SDGs 7 and 11 in Pakistan.
ABSTRACT
Growing energy consumption has been a contemporary problem, especially in the climate crisis and the COVID-19 pandemic. Many statistical reports have stated that there is an increase in energy consumption from residential households to the industrial sector. Electricity consumption forecasting is extremely important as it supports power system decision-making and management. In this paper, traditional ARIMAX and SARIMAX forecasting models and RNN-based deep learning models were used to model the electricity consumption historical data of a two-storied house located in Houston, Texas, USA. The features used in the modeling process include the daily-average electricity consumption historical data of the two-storied house, day category (weekday, weekend, vacation day, and COVID-lockdown), and weather-related variables. Each model's respective error performance on the testing dataset is compared. The result showed that RNN-based deep learning models outperformed the traditional ARIMAX and SARIMAX models in forecasting the daily-average electricity consumption of the two-storied house and that the performance of the RNN-based deep learning models doesn't differ significantly from each other. © 2022 IEEE.
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This research paper has shed light on the adverse effects of the covid-19 pandemic on our environment, which led us to lockdown at home and work or study remotely. Thus, American University of Ras-al-Khaimah was also closed, forcing all students to attend classes online. As a result of the online study model, the university environment has gradually changed. In actuality, this has impacted the environment significantly. Waste production has been drastically reduced, and electricity and water usage have been reduced significantly. Therefore, this paper focuses on electricity and water consumption, where a comparison study was conducted between electricity consumption in KWHs and water consumption in gallons before and after the pandemic. Furthermore, the fact that there are fewer faculty members and fewer students on campus will reflect a reduction in the amount of waste produced on campus. According to the study, the online study model of education uses less electricity and water, protecting the environment, so it is recommended that it be adopted in the future © 2022 IEEE.
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When the Indian government declared the first lockdown on 25 March 2020 to control the increasing number of COVID-19 cases, people were forced to stay and work from home. The aim of this study is to quantify the impact of stay-at-home orders on residential Air Conditioning (AC) energy and household electricity consumption (excluding AC energy). This was done using monitored data from 380 homes in a group of five buildings in Hyderabad, India. We gathered AC energy and household electricity consumption data at a 30-min interval for each home individually in April 2019 and April 2020. Descriptive and inferential statistical analysis was done on this data. To offset the difference in temperatures for the month of April in 2019 and 2020, only those weekdays were selected where the average temperature in 2019 was same as the average temperature in 2020. The study establishes that the average number of hours the AC was used per day in each home increased in the range 4.90–7.45% depending on the temperature for the year 2020. Correspondingly, the overall AC consumption increased in the range 3.60–4.5%, however the daytime (8:00 AM to 8:00 PM) AC energy consumption increased in the range 22–26% and nighttime (8:00 PM to 8:00 AM) AC energy consumption decreased by 5–7% in the year 2020. The study showed a rise in household electricity consumption of about 15% for the entire day in the year 2020. The household electricity consumption increased during daytime by 22- 27.50% and 1.90- 6.6% during the nighttime. It was observed that the morning household electricity peak demand shifted from 7:00 AM in 2019 to 9:00 AM in 2020. Conversely, the evening peak demand shifted from 9:00 PM in 2019 to 7:00 PM in 2020. An additional peak was observed during afternoon hours in the lockdown. © 2022, The Author(s).
ABSTRACT
The timing of electricity consumption is increasingly important for grid operations. In response, households are being encouraged to alter their daily usage patterns through demand response and time-varying pricing, although it is unknown if they are aware of these patterns. Here we introduce an energy literacy concept, 'load shape awareness', and apply it to a sample of California residents (n = 186) who provided their household's hourly electricity data and completed an energy use questionnaire. Choosing from four prominent load shape designations, half of respondents (51%) correctly identified their dominant load shape before COVID-19 shelter-in-place (SIP) orders while only one-third (31%) did so during SIP orders. Those aware of their load shape were more likely to have chosen evening peak, the most frequent dominant shape in the electricity data. Our work provides proof of principle for the load shape awareness concept, which could prove useful in designing energy conservation interventions and helping consumers adapt to an evolving energy system. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.
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We conduct a sectoral analysis of electricity consumption during the Coronavirus disease 2019 (COVID-19) pandemic for the primary sectors that make up Colombia's unregulated and regulated markets. Applying a model of seemingly unrelated regression equations to examine data between February 2015 and May 2021, we evidence the recomposition of electricity consumption related to mandatory preventive isolation during the pandemic. Average consumption in the residential sector increased by 16.9% as working from home became prevalent. In contrast, unregulated market sectors subjected to quarantines presented a significant decrease in consumption, up to 32% in the financial sector. While industries that were not subjected to mandatory confinement, such as health, food (agriculture), and water supply, had no significant effect. Our results are relevant for informing demand forecasts and planning network expansions to guarantee the reliability of the supply as pandemic practices such as working from home become permanent.
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The COVID-19 pandemic triggered a question of how to measure and evaluate adequacy of the applied restrictions. Available studies propose various methods mainly grouped to statistical and machine learning techniques. The current paper joins this line of research by introducing a simple-yet-accurate linear regression model which eliminates effects of weekly cycle, available daylight, temperature, and wind from the electricity consumption data. The model is validated using real data and enables the qualitative analysis of economical impact. © 2022 IEEE.
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The electric grid is uniquely susceptible to extreme events through both power supply and consumption pathways. Extreme events - like heatwaves and droughts - are expected to increase in frequency and severity due to climate change and are already causing consequences on power system operations and stability. Additionally, non-climate related events like the COVID-19 pandemic have had dramatic impacts on energy consumption patterns globally. We apply modern machine learning methods to model electricity consumption in Brazil, one of the largest generators of hydropower, to better understand the consumption-side effects of extreme national and regional events. After training on 20 years of historical data, we verify an R2of 0.848 and a MAPE of 2.6% for our counterfactual model and use it to assess impacts of historical events on electricity consumption. We then discuss how this approach can be applied toward measuring energy system responsiveness and resiliency on present and future scenarios. © 2022 IEEE.