Global ETD Search

1	Energy Consumption in COVID-19 Impact: Data Analysis and Deep Learning Modeling : master's thesis Мухаммед, А. А. М., Muhammad, A. A. M. January 2024 (has links) В этом исследовании изучаются беспрецедентные нарушения, вызванные пандемией COVID-19 в глобальных моделях потребления энергии. Используя ретроспективный подход, различные методологии, включая классическое машинное обучение и алгоритмы классификации временных рядов, используются для анализа энергетических данных, охватывающих период пандемии и после нее. Набор данных охватывает различные источники энергии, что позволяет изучать исторические тенденции как до, так и после COVID-19. Также изучаются региональные различия в моделях потребления энергии в ключевых регионах, таких как ОЭСР, БРИКС, СНГ и Ближний Восток. Конкретное исследование случая, сосредоточенное на Нью-Йорке, углубляется в тенденции потребления энергии в городе и влияние правил COVID-19. Представляя модель рекуррентной нейронной сети (RNN) для прогнозирования потребления энергии, исследование подчеркивает потенциал передовых методов моделирования в понимании и прогнозировании динамики потребления энергии. Применение модели RNN к данным о потреблении энергии в Нью-Йорке позволяет сравнивать прогнозируемые и фактические данные за 2020 год. Результаты подчеркивают значительные сдвиги в тенденциях глобального потребления энергии, раскрывая глубокое влияние пандемии на спрос на энергию и ее использование. Обсуждаются последствия этих сдвигов, подчеркивая необходимость адаптации энергетической политики и инфраструктуры к меняющемуся глобальному ландшафту. Рекомендации по будущим направлениям исследований предоставляются для улучшения понимания динамического взаимодействия между внешними потрясениями, такими как пандемии, и динамикой глобального потребления энергии. / 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. MASTER'S THESIS COVID-19 MACHINE LEARNING RECURRENT NEURAL NETWORKS (RNN) LENERGY CONSUMPTION GLOBAL ENERGY LANDSCAPE TIME SERIES ANALYSIS REGIONAL COMPARISONS NEW YORK CASE STUDY PANDEMIC IMPACT ENERGY POLICY COVID-19 ПОТРЕБЛЕНИЕ ЭНЕРГИИ МАШИННОЕ ОБУЧЕНИЕ ВЛИЯНИЕ ПАНДЕМИИ
2	Assessing foresight to advance management of complex global problems Berze, Ottilia E. 15 April 2019 (has links) Many people do not like thinking about the future. If they do, over 50% of Canadians think “our way of life” (p. 7) will end within 100 years and over 80% of Canadians think “we need to change our worldview and way of life if we are to create a better future for the world” (Randle & Eckersley, 2015, p. 9). There is a good reason for this. Alarms have sounded over global urgent complex problems with potential for catastrophic consequences such as the development of artificial intelligence, climate change, mass extinction, nuclear war and pandemics (Marien & Halal, 2011). Society is also increasingly fragmenting as imminent crises build on lack of understanding, the sense of incapacity to act, fear, distrust, blame and a lack of hope. This struggle for humanity’s survival is complicated by the turbulent global environment in which institutions continue to follow path-dependent trajectories set forth in a different time and context. Governments at various levels face a problem of “fit” between current structures and processes, that have not progressed sufficiently to meet changing needs of a global society mired in complexity and governance challenges. However, hope exists. Incremental progress on many fronts and a massive amount of efforts and resources are being engaged worldwide. There are emerging fields, lenses and tools that can potentially alleviate complex problems and address this emergency. The purpose of this dissertation is to understand and assess dialogue-based foresight practices being applied towards complex problems in Canada to provide insights into how these practices can assist society to alleviate global urgent complex problems and their impacts, within this backdrop of looming crises. Foresight, alternatively known as future studies or scenario-building, is a forward-looking practice recognized and used globally with over 100 research organizations focused on foresight, widespread usage by firms and over 18 countries involved in foresight activities (Berze, 2014b). Overall literature findings suggest foresight is widely and at least incrementally effective with a number of impacts in various areas (Calof, Miller, & Jackson, 2012; March, Therond, & Leenhardt, 2012; Meissner, Gokhberg, & Sokolov, 2013) but the extent of this effectiveness, the mechanisms involved, and the specific foresight benefits per type of project needs further research and evidence. For instance, limited literature exists on whether foresight can transform complex situations and if so, under what conditions. Thus, opportunities exist for assessing and increasing foresight’s impact. This dissertation is a contextualized, systematic empirical study that taps into transdisciplinary literature and practice, case studies of how foresight has been used to address specific types of complex problems in Canada, as well as surveys and interviews with foresight experts and participants. This dissertation uses a foresight community scan and a comparative case study approach to provide practical and theoretical benefits to foresight and complex problem area stakeholders. The research focuses on studying the broad interactions of foresight and identifying the impacts of dialogue-based foresight projects on people and the outcomes of complex problems. The dissertation concludes that dialogue-based foresight is a valuable and unique practice for ameliorating complex problems and their consequences. Insights are offered towards dialogue-based foresight’s potential contributions within the context of other efforts directed at humanity’s struggle for survival and global complex problems. These insights can then foster the further development and application of dialogue-based foresight on a global scale to alleviate complex problems and their effects. The dissertation outlines recommendations on key next steps to realize these potential contributions. / Graduate foresight dialogue-based complex global problems empirical transdisciplinary crisis comparative cases comparative case study hope practice community Canada projects impact value futures scenario future role wicked problems transformative complexity interviews urgent lens mechanisms assessment well-being strengthening improving concepts types foresight benefits contribution analysis tools approaches mainstream barriers Fore-CAN Georgia Basin Global Energy Landscape Agricultural Adaptation Canadian Payment System 2020 Media Futures systems digital climate change infectious diseases sustainability coping transitioning recommendations multi-perspective dialogue conflict resolution collaboration diversity outcomes invest context contributions efforts innovation cognitive social understanding foresight literacy anticipation futures literacy

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Energy Consumption in COVID-19 Impact: Data Analysis and Deep Learning Modeling : master's thesis

Assessing foresight to advance management of complex global problems