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SOCIAL NETWORK INFLUENCE ON RESIDENTIAL SOLAR PHOTOVOLTAIC ADOPTION IN AN AGENT-BASED ELECTRICITY SYSTEM MODEL

SOCIAL NETWORK INFLUENCE ON RESIDENTIAL SOLAR PHOTOVOLTAIC A...

ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： Russo, Gina Dello Wu, Lei Lytle, Ashley Odonkor, Philip School of Systems and Enterprises Stevens Institute of Technology HobokenNJ07030 United States Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States School of Humanities Arts and Social Sciences Stevens Institute of Technology HobokenNJ07030 United States School of Systems and Enterprises Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9780791888377

As the United States phases out traditional fossil fuels in favor of renewable energy sources, it is important to capitalize on all available avenues to increase renewable penetration. In the last decade, the costs associated with residential solar photovoltaic (PV) installations have decreased significantly, providing more homeowners with the opportunity to generate their own clean electricity. Research has found that the decision to invest in a residential solar PV system is guided by economic, social, and personal factors. Accounting for such complexities, the joint power of agent-based modeling and social network analysis is leveraged in this study to evaluate the effect of social influence on solar PV adoption. Featuring residential consumer agents with data-driven attributes, a logistic regression function to predict solar adoption, and random and small-world social network implementations, this work simulates residential solar PV adoption in New Jersey. Results indicate that including social influence in an agent-based electricity system model leads to increased installed residential solar capacity, but not necessarily higher adoption rates. These findings suggest that, with an understanding of the intricacies of consumer social networks, there are potential opportunities to bolster residential solar installations through low-cost social campaigns that motivate individuals to adopt home solar through their social ties. Copyright © 2024 by ASME.

关键词： Clean energy

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Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence

arXiv

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arXiv 2024年

作者： Pananjady, Ashwin Muthukumar, Vidya Thangaraj, Andrew Schools of Industrial and Systems Engineering Georgia Institute of Technology United States Electrical and Computer Engineering Georgia Institute of Technology United States Department of Electrical Engineering IIT Madras India

We study the problem of estimating the stationary mass—also called the unigram mass—that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has several applications—for example, estimating the stationary missing mass is critical for accurately smoothing probability estimates in sequence models. While the classical Good–Turing estimator from the 1950s has appealing properties for i.i.d. data, it is known to be biased in the Markovian setting, and other heuristic estimators do not come equipped with guarantees. Operating in the general setting in which the size of the state space may be much larger than the length n of the trajectory, we develop a linear-runtime estimator called Windowed Good–Turing (WingIt) and show that its risk decays as Oe(Tmix/n), where Tmix denotes the mixing time of the chain in total variation distance. Notably, this rate is independent of the size of the state space and minimax-optimal up to a logarithmic factor in n/Tmix. We also present an upper bound on the variance of the missing mass random variable, which may be of independent interest. We extend our estimator to approximate the stationary mass placed on elements occurring with small frequency in the trajectory. Finally, we demonstrate the efficacy of our estimators both in simulations on canonical chains and on sequences constructed from natural language text. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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Customizable Perturbation Synthesis for Robust SLAM Benchmarking

arXiv

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arXiv 2024年

作者： Xu, Xiaohao Zhang, Tianyi Wang, Sibo Li, Xiang Chen, Yongqi Li, Ye Raj, Bhiksha Johnson-Roberson, Matthew Huang, Xiaonan Department of Robotics University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States Department of Electrical and Computer Engineering Carnegie Mellon University United States

Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Simulation-based benchmarks have emerged as a highly scalable approach for robustness evaluation compared to real-world data collection. However, crafting a challenging and controllable noisy world with diverse perturbations remains relatively under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations. This pipeline incorporates customizable hardware setups, software components, and perturbed environments. In particular, we introduce comprehensive perturbation taxonomy along with a perturbation composition toolbox, allowing the transformation of clean simulations into challenging noisy environments. Utilizing the pipeline, we instantiate the Robust-SLAM benchmark, which includes diverse perturbation types, to evaluate the risk tolerance of existing advanced multi-modal SLAM models. Our extensive analysis uncovers the susceptibilities of existing SLAM models to real-world disturbance, despite their demonstrated accuracy in standard benchmarks. Our perturbation synthesis toolbox, SLAM robustness evaluation pipeline, and Robust-SLAM benchmark will be made publicly available at https://***/Xiaohao-Xu/SLAM-under-Perturbation/. © 2024, CC BY-NC-SA.

关键词： Pipelines

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Enhancing Fault Detection in Stochastic Environments Using Interval-Valued KPCA: A Cement Rotary Kiln Case Study

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IEEE Access 2025年 13卷 102951-102963页

作者： Abdelhalim Louifi Abdelmalek Kouadri Mohamed-Faouzi Harkat Abderazak Bensmail Majdi Mansouri Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M’Hamed Bougara of Boumerdes Boumerdes Algeria Systems and Advanced Material Laboratory Badji Mokhtar–Annaba University Annaba Algeria Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman

Fault detection in industrial processes is challenging due to significant data uncertainty, which complicates the accurate modeling of interval-valued data and the quantification of errors necessary for reliable detection. Existing approaches, such as kernel principal component analysis (KPCA), struggle with these challenges because they rely on single-valued data representations and are unable to effectively handle interval-based variability. To address these limitations, this paper introduces the interval-valued model KPCA (IV-KPCA), which extends KPCA by redefining similarity measures and kernel functions to accommodate interval-valued uncertainty. IV-KPCA preserves the interval structure throughout the modeling process, enhancing robustness to dynamic uncertainties and improving fault detection in complex nonlinear systems. Within this framework, fault detection statistics ( $T^{2}$ , Q, and $\Phi $ ) are developed to enable precise error quantification. The proposed method is validated on a cement rotary kiln process, a highly stochastic industrial system characterized by significant uncertainties. Experimental results demonstrate that IV-KPCA reduces false alarms, missed detections, and detection delays by over 100%, 90%, and 95%, respectively, compared to traditional methods. These findings underscore the potential of IV-KPCA in enhancing fault detection performance in complex, uncertain environments.

关键词： Fault detection Uncertainty Kernel Principal component analysis Data models Monitoring Computational modeling Accuracy Kilns Cement industry

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Policy Gradients for Probabilistic Constrained Reinforcement Learning 57

Policy Gradients for Probabilistic Constrained Reinforcement...

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57th Annual Conference on Information Sciences and systems, CISS 2023

作者： Chen, Weiqin Subramanian, Dharmashankar Paternain, Santiago Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States Ibm T. J. Watson Research Center Yorktown HeightsNY United States

ISBN: (纸本)9781665451819

This paper considers the problem of learning safe policies in the context of reinforcement learning (RL). In particular, we consider the notion of probabilistic safety. This is, we aim to design policies that maintain the state of the system in a safe set with high probability. This notion differs from cumulative constraints often considered in the literature. The challenge of working with probabilistic safety is the lack of expressions for their gradients. Indeed, policy optimization algorithms rely on gradients of the objective function and the constraints. To the best of our knowledge, this work is the first one providing such explicit gradient expressions for probabilistic constraints. It is worth noting that the gradient of this family of constraints can be applied to various policy-based algorithms. We demonstrate empirically that it is possible to handle probabilistic constraints in a continuous navigation problem. © 2023 IEEE.

关键词： Reinforcement learning

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Seer: A Framework for Optimizing Traffic Camera Placement and Deep Learning Inference at the Edge for Vehicle Path Reconstruction

Seer: A Framework for Optimizing Traffic Camera Placement an...

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作者： Siddhant Jain Kunal Jain Arun Ravindran Suresh Purini Computer Systems Group International Institute of Information TechnologyHyderabadIndia The University of North Carolina Department of Electrical and Computer EngineeringCharlotteUSA

Integrating traffic cameras with deep learning facilitates real-time multi-camera vehicle path reconstruction, benefiting smart city initiatives such as traffic management and public safety. The high demands on latency, bandwidth, and privacy underscore the necessity of edge computing platforms. However, the continuous operation of compute-intensive deep learning algorithms on round-the-clock camera streams can exceed the compute and power capacities of edge data centers. Therefore, it is critical to strategically deploy and activate camera streams to balance compute demands with performance requirements. Currently, there is a lack of tools to support this planning process. This paper presents Seer, a comprehensive suite of tools and algorithms that aids traffic planners in optimizing traffic camera placement and deep learning model deployment, considering compute and budgetary constraints. Seer encompasses (1) a graph-based algorithm for efficient camera placement incorporating road network topology, (2) a collaborative algorithm for vehicle path reconstruction that enables the use of less accurate but resource-efficient deep learning models, and (3) a scalable traffic simulation framework derived from open-source projects that model city road networks. Evaluations of Seer on two real-world road networks, each with thousands of intersections and simulated vehicle paths, show a 6.3x reduction in compute requirements. This is achieved through a combination of sparse camera deployment and the use of lightweight models, with an average 55% increase in the Hausdorff distance for the path reconstruction algorithm, translating to an absolute increase of 135 to 195 meters.

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Fast Mapping of Flexibility Regions at TSO-DSO Interfaces under Uncertainty

Fast Mapping of Flexibility Regions at TSO-DSO Interfaces un...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Patig, Alice Stanojev, Ognjen Aristidou, Petros Kiprakis, Aristides Hug, Gabriela Eth Eeh - Power Systems Laboratory Zurich Switzerland University of Edinburgh School of Engineering Institute for Energy Systems United Kingdom Cyprus University of Technology Computer Engineering and Informatics Department of Electrical Engineering Cyprus

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

The gradual decommissioning of fossil fuel-driven power plants, that traditionally provide most operational flexibility in power systems, has led to more frequent grid stability issues. To compensate for the lack of flexible resources, Distributed Energy Resources (DERs) in distribution networks can be employed. To facilitate the use of DERs, the aggregated flexibility in a distribution grid is commonly represented on a PQ-plane displaying the feasible active and reactive power exchange with the upstream grid. This paper proposes a fast feasible operating region mapping mechanism that utilizes a linear power flow approximation in combination with linearized generator, current, and voltage constraints to construct a high-dimensional polyhedral feasible set of DER power injections. The obtained polytope is projected onto the PQ-plane using Fourier-Motzkin Elimination to represent the aggregate network flexibility. Additionally, uncertainty in DER generation is addressed using chance-constraints. Our analysis of a modified 33-bus IEEE test system demonstrates that the proposed method obtains more accurate approximations than former geometric methods and is ten times faster than the tested optimization-based method. © 2022 IEEE.

关键词： Electric power distribution

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Autonomous helicopter aerial refueling: controller design and performance guarantees

arXiv

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arXiv 2025年

作者： Jayarathne, Damsara Paternain, Santiago Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

In this paper, we present a control design methodology, stability criteria and performance bounds for autonomous helicopter aerial refueling. Autonomous aerial refueling is particularly difficult due to the aerodynamic interaction between the wake of the tanker, the contact-sensitive nature of the maneuver, and the uncertainty in drogue motion. Since the probe tip is located significantly away from the helicopter’s center-of-gravity, its position (and velocity) is strongly sensitive to the helicopter’s attitude (and angular rates). In addition, the fact that the helicopter is operating at high speeds to match the velocity of the tanker forces it to maintain a particular orientation, making the docking maneuver especially challenging. In this paper, we propose a novel outer-loop position controller that incorporates the probe position and velocity into the feedback loop. The position and velocity of the probe tip depend both on the position (velocity) and on the attitude (angular rates) of the aircraft. We derive analytical guarantees for docking performance in terms of the uncertainty of the drogue motion and the angular acceleration of the helicopter, using the ultimate boundedness property of the closed-loop error dynamics. Simulations are performed on a high-fidelity UH60 helicopter model with a high-fidelity drogue motion under wind effects to validate the proposed approach for realistic refueling scenarios. These high-fidelity simulations reveal that the proposed control methodology yields an improvement of 36% in the 2-norm docking error compared to the existing standard controller. © 2025, CC BY.

关键词： Helicopters

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Integrated Data-Driven and Model-Based Trade-off Analysis of Sensor Suite System for Autonomous Ground Vehicle Navigation

Integrated Data-Driven and Model-Based Trade-off Analysis of...

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IEEE International Symposium on systems engineering (ISSE)

作者： Sai Sandeep Damera Praveen Kumar M. S John S. Baras Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA

ISBN: (数字)9798350353723

ISBN: (纸本)9798350353730

In recent years, the deployment of robots for commercial applications has surged, emphasizing the need to incorporate autonomy into these systems. The widespread adoption of autonomous robots across diverse fields thus necessitates a rigorous design framework. To address this, we recently proposed an Integrated Data-Driven and Model-Based systems engineering (IDDMBSE) framework—an extension of our traditional Model-Based systems engineering (MBSE). Our IDDMBSE framework aims to enable trusted autonomy in autonomous systems. In this work, we outline a methodology for trade-off analysis within the IDDMBSE framework, supporting the development of autonomous robots based on functional, performance, and programmatic requirements. The method comprises two key components: 1) Model-Based Optimization (MBO) - Leveraging domain-specific knowledge, MBO facilitates efficient simulation-based design, and 2) Data-Driven Evaluation (DDE) - Decoupling DDE from MBO allows effective exploration and statistical analysis of the design space. We demonstrate the utility of the proposed framework by using it to perform design space exploration and trade-off analysis for the design of the sensor suite of an autonomous ground robot. Our results show how a classical design problem that is often deemed intractable using model-based methods and unscalable using data-driven methods can benefit through the use of our hybrid framework.

关键词： Space vehicles Analytical models Statistical analysis Navigation Robot sensing systems Land vehicles Space exploration Optimization Autonomous robots

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