CAREER:Energy Management for Smart Residential Environments through Human-in-the-loop Algorithm Design

职业：通过人在环算法设计实现智能住宅环境的能源管理

• 批准号: 1943035
• 负责人: Simone Silvestri
• 金额: $ 52.5万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943035&HistoricalAwards=false
• 关键词: CAREER; Energy; Management; Smart; Residential

While substantial progress has been made in the control of electric grid considering the cyber and physical characteristics, there has been a gap in the integration of smart grid research as it integrates with human behavior -- especially in interactions with energy management systems. For example residential energy consumption has been rapidly increasing during the last decades, especially in the U.S. where 2.6 trillion kilowatt-hours were consumed during 2015, and an additional 13.5% increase is expected by 2040 . Research efforts such as demand response have been made to reduce this consumption especially in smart residential environments. Concepts such as demand response have largely overlooked the complexity of human behaviors and perceptions, and recent research in the social-science domain and recent experience has challenged the effectiveness of this approach and in some instances led to an abandonment and avoidance of such concepts. The objective of this proposal is to overcome the limitations associated with state-of-the-art energy management systems by designing novel algorithms, machine learning models, and optimization techniques that specifically consider user behaviors, perceptions, and psychological processes. This revolutionary approach will unleash the full potential of smart residential environments in reducing residential energy consumption and has the potential to transform the way in which energy management systems are designed, implemented, and used by people. This project also supports innovative educational activities such as classes, real time demonstrations, coding challenges, and research experiences for high school students. The PI will also lead a cohort of students to the diversity-oriented Grace Hopper conference and teach seminars for Hispanic elementary students. Finally, a new class on Cyber-Physical-Human System will be designed and several graduate and undergraduate students will participate in the research activities.The proposed research combines novel algorithmic, machine learning, and optimization solutions that consider previously un-examined human behaviors, perceptions, and psychological processes. Specifically, in order to enable fine grained energy monitoring, we propose novel stream-based appliance recognition algorithms for smart outlets. These algorithms learn the appliance consumption signatures and the user engagement with the system to optimize the learning process. In addition, energy saving optimization strategies are designed by considering the user perception through social-behavioral well-being models. These models learned and refined through novel machine learning algorithms based on regressograms, interpolation, and regression using user feedback provided through a smartphone. In addition, we develop optimization algorithms for energy exchange in the context of smart residential environments equipped with renewable energy generation. These algorithms match the users' demand and production, by considering and learning also their availability and preferences in the energy exchange process. The proposed research is validated through real testbeds and large-scale simulations based on real traces.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

虽然考虑到网络和物理特性，在电网控制方面取得了实质性进展，但在智能电网研究与人类行为的整合方面存在差距，特别是在与能源管理系统的交互方面。例如，在过去的几十年里，住宅能源消耗一直在迅速增长，尤其是在美国，2015年消耗了2.6万亿千瓦时，预计到2040年将再增加13.5%。为了减少这种消耗，特别是在智能住宅环境中，已经进行了需求响应等研究工作。需求响应等概念在很大程度上忽视了人类行为和感知的复杂性，最近在社会科学领域的研究和最近的经验对这种方法的有效性提出了挑战，在某些情况下导致放弃和回避这些概念。本提案的目标是通过设计新颖的算法、机器学习模型和优化技术来克服与最先进的能源管理系统相关的局限性，这些技术专门考虑用户行为、感知和心理过程。这种革命性的方法将释放智能住宅环境在降低住宅能耗方面的全部潜力，并有可能改变人们设计、实施和使用能源管理系统的方式。该项目还支持创新的教育活动，如课程、实时演示、编码挑战和高中学生的研究体验。PI还将带领一批学生参加以多元化为导向的Grace Hopper会议，并为西班牙裔小学生讲授研讨会。最后，我们将设计一门新的“网络-物理-人系统”课程，并邀请一些研究生和本科生参与研究活动。拟议的研究结合了新颖的算法、机器学习和优化解决方案，考虑了以前未经研究的人类行为、感知和心理过程。具体来说，为了实现细粒度的能量监测，我们提出了一种新的基于流的智能插座设备识别算法。这些算法学习设备消费签名和用户与系统的交互，以优化学习过程。此外，通过社会行为幸福感模型考虑用户感知，设计节能优化策略。这些模型通过新颖的机器学习算法进行学习和改进，这些算法基于回归图、插值和回归，使用智能手机提供的用户反馈。此外，我们开发了在配备可再生能源发电的智能住宅环境背景下的能源交换优化算法。这些算法通过考虑和学习用户在能源交换过程中的可用性和偏好来匹配用户的需求和生产。通过实际试验台和基于真实轨迹的大规模仿真验证了本文的研究结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Perceived-Value-driven Optimization of Energy Consumption in Smart Homes

• DOI: 10.1145/3375801
• 发表时间: 2020-04
• 期刊: ACM Transactions on Internet of Things
• 作者: A. R. Khamesi;S. Silvestri;D. A. Baker;Alessandra De Paola

Reproducibility of Survey Results: A New Method to Quantify Similarity of Human Subject Pools

调查结果的可重复性：量化人类受试者库相似性的新方法

• DOI: 10.1109/globecom42002.2020.9348076
• 发表时间: 2020
• 期刊: IEEE Global Communications Conference (GLOBECOM
• 作者: Khamesi, Atieh R.;Musmeci, Riccardo;Silvestri, Simone;Baker, D. A.
• 通讯作者: Baker, D. A.

Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing

• DOI: 10.1109/globecom48099.2022.10001173
• 发表时间: 2022-08
• 期刊: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
• 作者: Ashutosh Timilsina;S. Silvestri

V2G Optimization for Dispatchable Residential Load Operation and Minimal Utility Cost

V2G 优化可调度住宅负载运行和最低公用事业成本

• DOI: 10.1109/itec55900.2023.10186955
• 发表时间: 2023
• 期刊: IEEE
• 作者: Alden, Rosemary E.;Timilsina, Ashutosh;Silvestri, Simone;Ionel, Dan M.
• 通讯作者: Ionel, Dan M.

Reverse Auction-based Demand Response Program: A Truthful Mutually Beneficial Mechanism

• DOI: 10.1109/mass50613.2020.00059
• 发表时间: 2020-12
• 期刊: 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
• 作者: A. R. Khamesi;S. Silvestri