Transforming the Energy Sector: Addressing Key Challenges through Generative AI, Digital Twins, AI, Data Science and Analysis

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2024-01-11 DOI:10.4108/ew.4825

Praveen Tomar, Veena Grover

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Abstract

The energy sector, both in the UK and globally, faces significant challenges in the pursuit of sustainability and efficient resource utilization. Climate change, resource depletion, and the need for decarbonization demand innovative solutions. This analytical research paper examines the key challenges in the energy sector and explores how generative AI, digital twins, AI, and data science can play a transformative role in addressing these challenges. By leveraging advanced technologies and data-driven approaches, the energy sector can achieve greater efficiency, optimize operations, and facilitate informed decision-making. Artificial Intelligence (AI) involves replicating human-like intelligence in machines, enabling them to execute tasks that typically demand human cognitive capabilities like perception, reasoning, learning, and problem[1]solving. AI encompasses various methodologies and technologies, such as machine learning, natural language processing, computer vision, and robotics. Its adoption in the energy sector carries significant promise for addressing critical concerns and revolutionizing the industry. An overarching challenge in the energy sector revolves around enhancing energy efficiency, and AI emerges as a pivotal tool for optimizing energy utilization and curbing wastage. By analyzing vast amounts of data from various sources such as sensors, smart meters, and historical energy consumption patterns, AI algorithms can identify patterns and anomalies that humans may not detect. This enables the development of predictive models and algorithms that optimize energy consumption, leading to significant energy savings.

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变革能源行业：通过生成式人工智能、数字双胞胎、人工智能、数据科学与分析应对关键挑战

英国乃至全球的能源行业在追求可持续发展和有效利用资源方面都面临着重大挑战。气候变化、资源枯竭和去碳化的需求都需要创新的解决方案。本分析研究论文探讨了能源行业面临的主要挑战，并探讨了生成式人工智能、数字双胞胎、人工智能和数据科学如何在应对这些挑战方面发挥变革性作用。通过利用先进技术和数据驱动方法，能源行业可以实现更高的效率、优化运营并促进知情决策。人工智能（AI）是指在机器中复制类似人类的智能，使其能够执行通常需要人类认知能力的任务，如感知、推理、学习和解决问题[1]。人工智能包含各种方法和技术，如机器学习、自然语言处理、计算机视觉和机器人技术。将人工智能应用于能源领域，有望解决关键问题并彻底改变能源行业。能源行业的首要挑战是提高能源效率，而人工智能则是优化能源利用和减少浪费的关键工具。通过分析来自传感器、智能电表和历史能源消耗模式等各种来源的大量数据，人工智能算法可以识别人类可能无法发现的模式和异常。这样就能开发出优化能源消耗的预测模型和算法，从而节省大量能源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.