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IEEE Transactions on Energy Markets, policy and Regulation 2024年第4期2卷 519-535页

作者： Khanal, Saroj Graf, Christoph Liang, Zhirui Dvorkin, Yury Unel, Burcin Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States New York University School of Law Institute for Policy Integrity New YorkNY10012 United States StanfordCA94305 United States Johns Hopkins University Departments of Electrical and Computer Engineering and Civil and Systems Engineering BaltimoreMD21218 United States

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment. © 2024 IEEE.

关键词： Investments

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Predicting COVID-19 Transmission in Southern California with Machine Learning Methods 9

Predicting COVID-19 Transmission in Southern California with...

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9th International Conference on Big Data Analytics, ICBDA 2024

作者： Li, Han Wei, Ran Wang, Wenyu Yu, Nanpeng School of Computer Science and Engineering University of California Riverside Riverside United States School of Public Policy University of California Riverside Riverside United States University of California Riverside Department of Electrical and Computer Engineering Riverside United States

ISBN: (纸本)9798350352962

The COVID-19 pandemic has posed significant chal-lenges to global health, economies, and social structures since its emergence in late 2019. Southern California, a region with unique characteristics and diverse communities, has experienced difficul-ties in controlling the virus's spread. Understanding the factors that influence COVID-19 transmission in Southern California is crucial for informing public health policies and mitigating the virus's impact on the communities. In this study, we collected weekly zip code level data from March to December 2020 in Southern California and applied various machine learning algorithms, such as graph neural networks (GNN), multi-layer perceptrons (MLP), and gradient boosting trees (XGBoost), to model the spread of COVID-19. Our main contributions include the development of various COVID-19 forecasting models that incorporate socioeconomically significant variables and zip code level mobility data, coupled with variable impact assessments to provide valuable insights for informing future public health policies and strategies. Our predictive model not only attains high levels of accuracy in forecasting but also facilitates the understanding of how each variable contributes to the final outcome. These findings have the potential to enhance our understanding of COVID-19 transmission patterns and inform targeted mitigation strategies in the region. © 2024 IEEE.

关键词： COVID-19

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A Comprehensive Exploration of Renewable Energy Education Landscape in Ethiopian public Universities

A Comprehensive Exploration of Renewable Energy Education La...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Temesgen, Adugnaw Lake Bekele, Getachew Broto, Vanesa Castan Mulugeta, Yacob School of Electrical and Computer Engineering Addis Ababa University Addis Ababa Ethiopia Urban Insctitute University of Sheffield Sheffield United Kingdom University College London Department of Science Technology Engineering and Public Policy London United Kingdom

ISBN: (纸本)9798350389388

The renewable energy sector has experienced a rapid expansion, driven by rising fossil fuel costs, increasing concerns about energy security, and the imperative to electrify rural areas. This growth creates a demand for skilled professionals, and higher education institutions must respond by providing comprehensive and practical renewable energy education. This study aims to analyze energy education in Ethiopian public universities, considering program offerings, content, geographical distribution, challenges, and opportunities. To achieve this, a multifaceted methodology involving systematic curriculum reviews, targeted surveys, data compilation, and interactive spatial mapping is employed. Findings indicate a diverse landscape, with both standalone programs and integrated courses available. Among the 45 public universities, only 22% of them have Institutes of Technologies (IoTs), indicating a significant technology-focused institute gap. None of these universities provide standalone energy programs at the BSc level, and less than 10% offer such programs at the MSc level. Notably, key energy courses like 'Energy Conversion and Rural Electrification' and 'Hydropower engineering' have been identified, taught in 78% of public universities, and integrated within electrical Power engineering programs at the BSc level. However, the evident lack of practical training and experiential learning in energy education has implications for the sector's development in Ethiopia. © 2024 IEEE.

关键词： Clean energy

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Machine Learning Classification of Snowflakes to Enhance Microphysical and Scattering Characterization of Snow

Machine Learning Classification of Snowflakes to Enhance Mic...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Thant, Hein Zhizhin, Mikhail Notaros, Branislav M. Colorado State University Electrical & Computer Engineering Dept. Fort CollinsCO United States Colorado School of Mines Payne Institute for Public Policy GoldenCO United States

ISBN: (纸本)9781665442282

No two snowflakes are alike. However, they can be classified into over a hundred different categories based on their geometry. Recent developments in machine learning algorithms have led to the possibility of automatically classifying more classes of snowflakes more accurately and efficiently. We present supervised and unsupervised learning approaches to snowflake classification coupled with dimensional reduction techniques. Proper automatic and detailed classification of ice and snow hydrometeors enables advanced characterization of geometrical, microphysical, and scattering properties of particles, which, in turn, is essential for development of radar-based quantitative precipitation estimation of snow. © 2023 IEEE.

关键词： Snow

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Predicting COVID-19 Transmission in Southern California with Machine Learning Methods

Predicting COVID-19 Transmission in Southern California with...

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Han Li Ran Wei Wenyu Wang Nanpeng Yu School of Computer Science and Engineering University of California Riverside Riverside USA School of Public Policy University of California Riverside Riverside USA Department of Electrical and Computer Engineering University of California Riverside Riverside USA

ISBN: (数字)9798350352962

ISBN: (纸本)9798350352979

关键词： COVID-19 Analytical models Pandemics Sociology Predictive models Data models public healthcare

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Ethical Issues in Near-Future Socially Supportive Smart Assistants for Older Adults

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2023年第4期4卷 291-301页

作者： London, Alex John Razin, Yosef S. Borenstein, Jason Eslami, Motahhare Perkins, Russell Robinette, Paul Carnegie Mellon University Department of Philosophy PittsburghPA15213 United States Georgia Institute of Technology School of Aerospace Engineering AtlantaGA30332 United States Georgia Institute of Technology School of Public Policy and Office of Graduate Education AtlantaGA30332 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States University of Massachusetts Lowell Department of Electrical and Computer Engineering LowellMA01854 United States

This paper considers novel ethical issues pertaining to near-future artificial intelligence (AI) systems that seek to support, maintain, or enhance the capabilities of older adults as they age and experience cognitive decline. In particular, we focus on smart assistants (SAs) that would seek to provide proactive assistance and mediate social interactions between users and other members of their social or support networks. Such systems would potentially have significant utility for users and their caregivers if they could reduce the cognitive load for tasks that help older adults maintain their autonomy and independence. However, proactively supporting even simple tasks, such as providing the user with a summary of a meeting or a conversation, would require a future SA to engage with ethical aspects of human interactions which computational systems currently have difficulty identifying, tracking, and navigating. If SAs fail to perceive ethically relevant aspects of social interactions, the resulting deficit in moral discernment would threaten important aspects of user autonomy and well-being. After describing the dynamic that generates these ethical challenges, we note how simple strategies for prompting user oversight of such systems might also undermine their utility. We conclude by considering how near-future SAs could exacerbate current worries about privacy, commodification of users, trust calibration and injustice. © 2020 IEEE.

关键词： Cognitive systems

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From Lab to Field: Real-World Evaluation of an Ai-Driven Smart Video Solution to Enhance Community Safety

SSRN

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SSRN 2024年

作者： Yao, Shanle Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bourque, Lauren Tabkhi, Hamed University of North Carolina at Charlotte Department of Electrical and Computer Engineering United States University of North Carolina at Charlotte Department of Public Policy United States

This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose based data is used for downstream AI tasks such as anomaly detection. Cloud-based infrastructure and mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird's Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex computer vision outputs into actionable insights for stakeholders, community partners, law enforcement, urban planners, and social scientists. This article presents a comprehensive real-world deployment and evaluation of the SVS, implemented in a community college environment across 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system's robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-hour period and an average end-to-end latency of 26.76 seconds between anomaly detection and alert issuance. © 2024, The Authors. All rights reserved.

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Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark

arXiv

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arXiv 2024年

作者： Pazho, Armin Danesh Yao, Shanle Noghre, Ghazal Alinezhad Ardabili, Babak Rahimi Katariya, Vinit Tabkhi, Hamed Department of Electrical and Computer Engineering University of North Carolina Charlotte United States Public Policy Program University of North Carolina Charlotte United States

Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the complexity of diverse human behaviors, the rarity of anomalies, and ethical constraints. These challenges limit access to high-quality datasets and highlight the need for a dataset and framework supporting continual learning. Moving towards adaptive human-centric VAD, we introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time, bridging traditional training and real-world deployment. HuVAD prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes, offering over 5× more pose-annotated frames than previous datasets. Our standard and continual benchmarks, utilize a comprehensive set of metrics, demonstrating that UCAL-enhanced models achieve superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at https://***/TeCSAR-UNCC/HuVAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Operational Modeling of the Nexus Between Water-Distribution and Electricity Systems

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Current Sustainable/Renewable Energy Reports 2025年第1期12卷 1-16页

作者： Gu, Weiying Sioshansi, Ramteen Department of Integrated Systems Engineering The Ohio State University 1971 Neil Avenue ColumbusOH43210-1271 United States Department of Engineering and Public Policy Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Carnegie Mellon Electricity Industry Center Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Wilton E. Scott Institute for Energy Innovation Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States

Purpose of Review: We survey operational models of water-distribution systems. Although such modeling is important in its own right, our focus is motivated by the growing desire to examine and manage the nexus between water-distribution and electricity systems. As such, our survey discusses computational challenges in modeling water-distribution systems, co-ordination dynamics between water-distribution and electricity systems, and gaps in the literature. Recent Findings: Modeling water-distribution systems is made difficult by their highly non-linear and non-convex physical properties. Co-ordinating water-distribution and electricity systems, especially with the growing supply and demand uncertainties of the latter, requires fast optimization techniques for real-time system management. Although many works suggest means of co-ordinating the two systems, practical applications are limited, due to the systems having separate and autonomous management and ownership. Nonetheless, recent works are navigating this challenge, by seeking methods to foster improved co-ordination of the two systems while respecting their autonomy. Additionally, with the backdrop of increased security threats, there is a growing need to bolster infrastructure protection, which is complicated by the intertwined nature of the two systems. Summary: By providing a steady supply of potable water to satisfy residential, commercial, agricultural, and industrial demands, water-distribution systems are pivotal components of modern society and infrastructure. The extant literature presents many models and optimization strategies that are tailored for operating water-distribution systems. Yet, there remain unexplored problems, particularly related to simplifying model computation, capturing the flexibility of water-distribution systems, and capturing interdependencies between water-distribution and other systems and infrastructures. Future research that addresses these gaps will allow greater operational

关键词： Water distribution systems

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cpp11armadillo: An R Package to Use the Armadillo C++ Library

arXiv

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arXiv 2024年

作者： Sepúlveda, Mauricio Vargas Malamud, Jonathan Schneider Department of Political Science University of Toronto Munk School of Global Affairs and Public Policy University of Toronto Canada Department of Electrical and Computer Engineering University of Toronto Canada

This article introduces ‘cpp11armadillo’, a new R package that integrates the powerful Armadillo C++ library for linear algebra into the R programming environment. Targeted primarily at social scientists and other non-programmers, this article explains the computational benefits of moving code to C++ in terms of speed and syntax. We provide a comprehensive overview of Armadillo’s capabilities, highlighting its user-friendly syntax akin to MATLAB and its efficiency for computationally intensive tasks. The ‘cpp11armadillo’ package simplifies a part of the process of using C++ within R by offering additional ease of integration for those who require high-performance linear algebra operations in their R workflows. This work aims to bridge the gap between computational efficiency and accessibility, making advanced linear algebra operations more approachable for R users without extensive programming backgrounds. © 2024, CC BY-NC-SA.

关键词： MATLAB

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