Collaborative Research: Crosslayer Optimization of Energy and Cost through Unified Modeling of User Behavior and Storage in Multiple Buildings

协作研究：通过对多个建筑物中的用户行为和存储进行统一建模来实现能源和成本的跨层优化

• 批准号: 1936131
• 负责人: Simone Silvestri
• 金额: $ 33.33万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936131&HistoricalAwards=false
• 关键词: Collaborative; Research; Crosslayer; Optimization; Energy

The building sector is the largest energy consumer in the world, and in the United States it accounts for more than 40 percent of the total energy consumption and greenhouse gas emissions. Therefore, it is economically, socially, and environmentally important to reduce the energy consumption of this sector. The goal of this collaborative proposal is to develop novel machine learning based algorithms to address the problem of energy optimization at the building and district levels. These algorithms are integrated within a simulation framework that combines user behavior with the collaboration between buildings equipped with photovoltaic arrays, energy storage systems, and smart grid meters. The proposed research is expected to lay the foundation for robust multi-objective optimization for next generation district level distribution systems. The proposed research is closely integrated with a broad and diverse education and outreach plan aimed at inspiring young women to pursue careers in STEM through summer programs for middle school. Additionally, the project will train the next generation of engineers and researchers by involving graduate and undergraduate students through the proposed research as well as through classes taught by the PIs encompassing the proposed research methodologies. Overall, the outcomes of this proposal are expected to significantly advance the areas of energy optimization, electric power systems, and smart grid design, as well as to have a positive impact on the academic and industrial communities and society.The project proposes and integrates, within the same software tool, novel machine learning models for complex user behavior at the individual building level, for energy load prediction and energy storage systems scheduling at the district level, and for cost reduction via energy peak spreading. These models are used to formulate and construct algorithmic solutions based on reinforcement learning, recurrent and deep neural networks, and deep reinforcement learning suitable for implementation in the future generation Virtual Power Plants. The methodologies employed for energy reduction and cost minimization include: 1) alter user behavior through personalized recommendations regarding changes in the appliance states (e.g., heating and air conditioning settings), 2) district-level scheduling of energy storage systems among buildings equipped with photovoltaic arrays and smart grid meters, and 3) building-level scheduling of energy consumption events for smart appliances equipped with smart Internet-of-Things controllers to take benefit of different energy prices.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.

建筑行业是世界上最大的能源消耗者，在美国，它占总能源消耗和温室气体排放的40%以上。因此，减少该部门的能源消耗在经济、社会和环境方面都具有重要意义。这项合作提案的目标是开发新的基于机器学习的算法，以解决建筑和地区层面的能源优化问题。这些算法集成在一个模拟框架中，该框架将用户行为与配备光伏阵列、能源存储系统和智能电网仪表的建筑物之间的协作结合起来。本研究可望为下一代区级配电系统的鲁棒多目标优化奠定基础。拟议的研究与一项广泛而多样化的教育和推广计划密切相关，该计划旨在通过中学暑期课程激励年轻女性在STEM领域追求职业。此外，该项目还将培养下一代工程师和研究人员，包括研究生和本科生，通过拟议的研究，以及通过pi教授的课程，包括拟议的研究方法。总体而言，本提案的成果有望显著推进能源优化、电力系统和智能电网设计领域，并对学术界、工业界和社会产生积极影响。该项目在同一软件工具中提出并集成了新的机器学习模型，用于单个建筑层面的复杂用户行为，用于区域层面的能源负荷预测和储能系统调度，以及通过能源峰值扩展降低成本。这些模型用于制定和构建基于强化学习、循环和深度神经网络、深度强化学习的算法解决方案，适合在下一代虚拟电厂中实施。用于减少能源和降低成本的方法包括：1)通过对家电状态变化（如供暖和空调设置）的个性化建议来改变用户行为；2)在安装光伏阵列和智能电表的建筑物之间进行储能系统的区域级调度；3)在安装智能物联网控制器的智能家电之间进行能耗事件的建筑级调度，以利用不同的能源价格。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.

P2P Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning

通过前景理论、差分进化和强化学习进行 P2P 能源交易

• DOI: 10.1145/3603148
• 发表时间: 2023
• 期刊: ACM Transactions on Evolutionary Learning and Optimization
• 作者: Timilsina, Ashutosh;Silvestri, Simone
• 通讯作者: Silvestri, Simone

Dissecting the Problem of Individual Home Power Consumption Prediction using Machine Learning

使用机器学习剖析个人家庭功耗预测问题

• DOI: 10.1109/smartcomp55677.2022.00037
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Smart Computing (SMARTCOMP
• 作者: Casella, Enrico;Sudduth, Eleanor;Silvestri, Simone
• 通讯作者: Silvestri, Simone

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