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The escalating frequency and sophistication of cyber threats increased the need for their comprehensive understanding. This paper explores the intersection of geopolitical dynamics, cyber threat intelligence analysis, and advanced detection technologies, with a focus on the energy domain. We leverage generative artificial intelligence to extract and structure information from raw cyber threat descriptions, enabling enhanced analysis. By conducting a geopolitical comparison of threat actor origins and target regions across multiple databases, we provide insights into trends within the general threat landscape. Additionally, we evaluate the effectiveness of cybersecurity tools -- with particular emphasis on learning-based techniques -- in detecting indicators of compromise for energy-targeted attacks. This analysis yields new insights, providing actionable information to researchers, policy makers, and cybersecurity professionals.

The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment List for Trustworthy AI (ALTAI), the AI act, and relevant CEN-CENELEC standardization efforts, as well as EU-funded projects such as AI4EU and SHERPA. Subsequently, we introduce a new TAI application framework, called E-TAI, tailored for energy applications, including smart grid and smart building systems. This framework draws inspiration from EGTAI but is customized for AI systems in the energy domain. It is designed for stakeholders in electrical power and energy systems (EPES), including researchers, developers, and energy experts linked to transmission system operators, distribution system operators, utilities, and aggregators. These stakeholders can utilize E-TAI to develop and evaluate AI services for the energy sector with a focus on ensuring trustworthiness throughout their development and iterative assessment processes.

To tackle the AVeriTeC shared task hosted by the FEVER-24, we introduce a system that only employs publicly available large language models (LLMs) for each step of automated fact-checking, dubbed the Herd of Open LLMs for verifying real-world claims (HerO). For evidence retrieval, a language model is used to enhance a query by generating hypothetical fact-checking documents. We prompt pretrained and fine-tuned LLMs for question generation and veracity prediction by crafting prompts with retrieved in-context samples. HerO achieved 2nd place on the leaderboard with the AVeriTeC score of 0.57, suggesting the potential of open LLMs for verifying real-world claims. For future research, we make our code publicly available at https://github.com/ssu-humane/HerO.

The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivity, and streamline future operations of engineered installations in a highly competitive industry. The current AI market offers various solutions and services to support this industry, but organizations must understand how to acquire AI technology in the most beneficial way based on their business strategy and available resources. This paper presents a framework for EPC companies in their transformation towards AI. Our work is based on examples of project execution of AI-based products development at one of the biggest EPC contractors worldwide and on insights from EPC vendor companies already integrating AI into their engineering solutions. The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework identifies how various factors influence the choice of approach toward AI project development within large international engineering corporations. By presenting a practical guide for optimal approach selection, this paper contributes to the research in AI project management and organizational strategies for integrating AI technology into businesses. The framework might also help engineering companies choose the optimum AI approach to create business value.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' Lawmakers on the Senate Energy Committee were warned on Thursday about both the threats and opportunities that come with artificial intelligence being integrated into the U.S. energy sector and everyday life as a whole. The committee held a hearing on the rapidly advancing technology, and experts present spent a significant amount of time not only discussing AI but the ever-looming threat of China and its efforts to steal and recreate emerging U.S. capabilities. "China released their new generation of AI Development Plan, which includes [research and development] and infrastructure targets. The U.S. currently does not have a strategic AI plan like this," Committee Chair Joe Manchin, D-W.Va., said at the hearing's outset.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' The top Republican on the Senate Energy Committee will warn Thursday against allowing U.S. artificial intelligence capabilities to fall into China's hands when the panel meets for a hearing on the topic. Senators returned to Capitol Hill just days ago after spending the month of August in their home states. AI is expected to be a prominent topic for lawmakers as they race to get ahead of the rapidly advancing technology. It's also the topic at the heart of Thursday's hearing led by Energy Committee Chair Joe Manchin, D-W.Va., and ranking member John Barrasso, R-Wyo., that aims to examine how AI has affected the U.S. energy sector and how the federal government can stay competitive in that lane.

The global artificial intelligence (AI) in energy market size was valued at $4.0 billion in 2021, and is projected to reach $19.8 billion by 2031, growing at a CAGR of 17.4% from 2022 to 2031. The developed and developing nations are adopting artificial intelligence in energy sector and are highly contributing towards the growth of the market during the forecast period. Measuring greenhouse gas emissions with AI enabled drones, accelerating energy transition and optimizing geothermal energy are the strategies being adopted by major players that are operating in the artificial intelligence in the energy market which may enhance the market position and boost the growth of the market over the forthcoming years. Artificial intelligence (AI) is an extent of computer technology that emphasizes the formation of intelligent machines which works and reacts such as humans. In addition, considering advanced technologies, it creates significant changes and effects of AI technology in the energy and utility market that can be seen in their applications such as smart automated grid and energy distribution system.

As one of Ireland's leading renewable energy developers and suppliers of green electricity, Energia Group proudly employs numerous scientists working across various fields, from marine biology to data science. Ahead of Science Week, which runs from the 13th to the 20th of November, Energia Group is celebrating the infinite possibilities and the role of science in the energy sector and how that contributes to Ireland's climate goals. Neil Mc Caul, Gregory Balogh and Anchit Bhagat are working together on an innovative artificial intelligence solution that can support energy traders in the decisions they make. Neil Mc Caul, Energy Trading Development Manager with Energia Group has more than 15 years energy trading experience. He has seen first-hand how the rapid increase in digitalization plus the added complexity of many additional energy sources such as solar, increased levels of wind (both onshore & offshore) and battery storage has changed the energy trading industry.

This article grants a comprehensive understanding of the applications and prospects of AI Blockchain technology in smart cities. I have researched and compiled information regarding the Internet of things (IoT) technologies for smart cities, smart cities as innovation ecosystems sustained by the future internet, traffic management by automation of street lights, and the future of waste management in sustainable cities. The implementation of these technologies has shown a decrease in carbon emissions, traffic, and error in recycling systems. Blockchain applications in smart cities have been extensively researched in order to clearly explain the immense influence this technology will have on all future data-related activities. This includes but is not limited to, governments, banks, hospitals, civilian services, and energy trades. The literature compiled gives a closer understanding of how AI blockchain is a viable option for batching and automating the analysis of large data packets, to allow for streamlined damage control and a massive reduction in outbreaks. The material showcases how a city's eco-system could benefit and sustain itself using these innovative IT solutions. It also gives insight into how individuals could begin profiting off tangible assets once these technologies are implemented. As the populace in cities becomes considerably denser, governments and civil engineers are looking for more modern solutions for monitoring city operations such as traffic, waste management, and air quality.