PIRE: Building Decarbonization via AI-empowered District Heat Pump Systems

PIRE：通过人工智能支持的区域热泵系统实现脱碳

• 批准号: 2230748
• 负责人: Zheng O'Neill
• 金额: $ 150万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230748&HistoricalAwards=false
• 关键词: PIRE; Building; Decarbonization; via; AI

Increasing concerns about climate change, clean energy, and energy security demand that our society transition to a net-zero carbon economy that serves the triple bottom line of planetary health, societal well-being, and economic prosperity. This PIRE project is focused on innovating Artificial Intelligence (AI) techniques with an understanding of human need and behaviors to enable an efficient, human-centered, resilient, and socially justifiable operation of district- and community-scale heat pumps systems that promote a regional scale adoption of building decarbonization. In an increasingly urbanized world, there is a pressing need to address the critical challenges of climate change through the built environment because the building sector accounts for nearly 40% of the primary energy use in the U.S. and associated greenhouse gas and CO2 emissions, with about 50% of that energy dedicated to heating, cooling, ventilation, and lighting. In addition, people spend more than 90% of their time indoors which means that addressing their needs and comfort in a sustainable way is critical for climate resilience planning. Electrification of heating and cooling systems is widely acknowledged as a core and non-negotiable strategy for decarbonization. Many major U.S. metropolitan areas have put the adoption of electric heat pump technologies on the roadmap reaching building decarbonization in the next decade. Taking advantage of the wide adoption of district heating/cooling heat pump systems in Nordic countries, this project seeks to leverage the data and testbeds provided by our core international partners from Sweden and Denmark and the AI innovations provided by the U.S. team to catalyze the readiness to support the scaling up and adoption of human-centered and equity focused AI empowered district system operation strategies at a regional and global scale. Findings from this project will be disseminated through two International Energy Agency’s Annex teams (81 and 84) which will reach to researchers from more than 20 countries. Through their outreach activities, including focus group discussions and workshops, the team will work closely with partners from government and community stakeholders to promote community-focused and equitable district heat pump adoption, implementation, and operation. Two new cross-institutional education programs are designed to promote convergent international education in human centered sustainable built environment: a) Summer International Graduate Bootcamp and Exchange Program; and b) Smart Built Environment Certification Program. Leveraging partner institutions’ other existing flagship programs ranging from K-12, undergraduate, and workforce training, the project will train a diverse, convergent workforce well-versed in science, technology, engineering, arts, and mathematics (STEAM), AI, and socioeconomics to tackle global challenges of climate change.This project contributes to: 1) data science – the development of new human-interactive AI tools to help facility managers and building owners (i.e., users) to make timely decisions and to help incentivize a faster and wider adoption of building decarbonization; 2) building science – the development of novel and scalable models and algorithms for occupant-centric and socially equitable controls driven by occupant, environmental, and community needs; and 3) social science – engaging communities and decision-makers in the design of AI systems to incorporate their environmental, social, economic, and equity needs in the models. Specific anticipated engineering/science contributions include: 1) novel causally-informed Bayesian network with dynamic causal discovery and thermography-based big data analysis for a scalable performance monitoring, energy diagnosis, and prognosis of district heat pump systems, 2) a comprehensive AI learning based occupant needs and behavior modeling framework to bridge the gaps within human-building-community interactions and promote energy equity across the district, 3) new physics- and data-informed learning models for forecasting and optimization under uncertainty, and 4) holistic data sets collected from pilot sites in Sweden/Denmark and the U.S. that use an energy justice lens and are developed through a community-informed approach. The project team's international industry partners, including a heat pump manufacturer and district energy system management companies, will guide the project to streamline the potential technology transfer process.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.

对气候变化、清洁能源和能源安全的日益关注要求我们的社会向净零碳经济过渡，以服务于地球健康、社会福祉和经济繁荣的三重底线。该项目专注于创新人工智能（AI）技术，了解人类的需求和行为，使地区和社区规模的热泵系统高效、以人为本、有弹性和社会合理的运行，从而促进区域范围内建筑脱碳的采用。在日益城市化的世界中，迫切需要通过建筑环境来解决气候变化的关键挑战，因为建筑行业占美国一次能源使用的近40%，并产生相关的温室气体和二氧化碳排放，其中约50%的能源用于供暖、制冷、通风和照明。此外，人们90%以上的时间都在室内度过，这意味着以可持续的方式满足他们的需求和舒适度对于气候适应规划至关重要。加热和冷却系统的电气化被广泛认为是脱碳的核心和不可谈判的战略。美国许多大城市已经将采用电热泵技术列入未来十年实现建筑脱碳的路线图。利用北欧国家广泛采用区域供热/制冷热泵系统的优势，该项目旨在利用我们来自瑞典和丹麦的核心国际合作伙伴提供的数据和测试平台，以及美国团队提供的人工智能创新，促进准备工作，支持在区域和全球范围内扩大和采用以人为本、以公平为重点的人工智能授权区域系统运营战略。该项目的调查结果将通过国际能源署的两个附件小组（81和84）传播，这两个小组将接触到来自20多个国家的研究人员。通过他们的外展活动，包括焦点小组讨论和研讨会，小组将与政府和社区利益相关者密切合作，促进以社区为中心和公平的区域热泵的采用、实施和运营。两个新的跨机构教育项目旨在促进以人为本的可持续建筑环境的融合国际教育：a)夏季国际研究生训练营和交流计划；b)智能建筑环境认证计划。利用合作院校现有的其他旗舰项目，包括K-12、本科和劳动力培训，该项目将培养一支精通科学、技术、工程、艺术和数学（STEAM）、人工智能和社会经济学的多元化、融合型劳动力，以应对气候变化的全球挑战。该项目有助于：1)数据科学——开发新的人机交互人工智能工具，帮助设施管理者和建筑业主（即用户）及时做出决策，并帮助激励更快、更广泛地采用建筑脱碳；2)建筑科学——开发新颖的、可扩展的模型和算法，以满足以居住者为中心的、由居住者、环境和社区需求驱动的社会公平控制；3)社会科学——让社区和决策者参与人工智能系统的设计，将他们的环境、社会、经济和公平需求纳入模型。具体预期的工程/科学贡献包括：1)基于动态因果关系发现和基于热像仪的大数据分析的新型因果关系贝叶斯网络，用于可扩展的区域热泵系统性能监测、能源诊断和预测；2)基于综合人工智能学习的居住者需求和行为建模框架，以弥合人-建筑-社区互动中的差距，促进整个区域的能源公平；3)新的基于物理和数据的学习模型，用于不确定性下的预测和优化；4)从瑞典/丹麦和美国的试点地点收集的整体数据集，这些数据集使用能源正义的视角，并通过社区知情的方法开发。项目团队的国际行业合作伙伴，包括一家热泵制造商和区域能源系统管理公司，将指导该项目简化潜在的技术转让过程。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。