Energy Consumption in COVID-19 Impact: Data Analysis and Deep Learning Modeling
Аннотация
This research investigates the unprecedented disruptions caused by the COVID-19 pandemic on global energy consumption patterns. Employing a retrospective approach, diverse methodologies including classic machine learning and time series classification algorithms are utilized to analyze
energy data spanning the pandemic period and beyond. The dataset encompasses various energy sources, enabling examination of historical trends both pre and post-COVID-19.
Regional disparities in energy consumption patterns across key regions like OECD, BRICS, CIS, and the Middle East are also explored. A specific case study focusing on New York delves into the city's energy consumption trends and the impact of COVID-19 regulations. Introducing a Recurrent Neural Network (RNN) model for energy consumption prediction, the study highlights the potential of advanced modeling techniques in understanding and forecasting energy usage dynamics.
Application of the RNN model to New York's energy consumption data allows comparison between predicted and actual 2020 data. The findings underscore significant shifts in global energy consumption trends, revealing the pandemic's profound impact on energy demand and utilization. Implications of these shifts are discussed, emphasizing the necessity of adapting energy policies and infrastructure to the evolving global landscape. Recommendations for future research directions are
provided to enhance comprehension of the dynamic interplay between external shocks, such as pandemics, and global energy consumption dynamics.
Keywords: COVID-19, energy consumption, global energy landscape, machine learning, time series analysis, Recurrent Neural Network (RNN), regional comparisons, New York case study, pandemic impact, energy policy.
energy data spanning the pandemic period and beyond. The dataset encompasses various energy sources, enabling examination of historical trends both pre and post-COVID-19.
Regional disparities in energy consumption patterns across key regions like OECD, BRICS, CIS, and the Middle East are also explored. A specific case study focusing on New York delves into the city's energy consumption trends and the impact of COVID-19 regulations. Introducing a Recurrent Neural Network (RNN) model for energy consumption prediction, the study highlights the potential of advanced modeling techniques in understanding and forecasting energy usage dynamics.
Application of the RNN model to New York's energy consumption data allows comparison between predicted and actual 2020 data. The findings underscore significant shifts in global energy consumption trends, revealing the pandemic's profound impact on energy demand and utilization. Implications of these shifts are discussed, emphasizing the necessity of adapting energy policies and infrastructure to the evolving global landscape. Recommendations for future research directions are
provided to enhance comprehension of the dynamic interplay between external shocks, such as pandemics, and global energy consumption dynamics.
Keywords: COVID-19, energy consumption, global energy landscape, machine learning, time series analysis, Recurrent Neural Network (RNN), regional comparisons, New York case study, pandemic impact, energy policy.