1239257 (Wu). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations. http://swag.engineering.asu.edu/ 1239247 (Wen). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations. http://swag.engineering.asu.edu/ 1239093 (Lewis). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations.

This project explores balancing performance considerations and power consumption in cyber-physical systems, through algorithms that switch among different modes of operation (e.g., low-power/high-power, on/off, or mobile/static) in response to environmental conditions. The main theoretical contribution is a computational, hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components. The fundamental research in this program advances state-of-the-art along four different dimensions, namely (1) real-time, hybrid optimal control algorithms for power management, (2) power-management in mobile sensor networks, (3) distributed power-aware architectures for infrastructure management, and (4) power-management in embedded multi-core processors. The expected outcome, which is to enable low-power devices to be deployed in a more effective manner, has implications on a number of application domains, including distributed sensor and communication networks, and intelligent and efficient buildings. The team represents both a research university (Georgia Institute of Technology) and an undergraduate teaching university (York College of Pennsylvania) in order to ensure that the educational components are far-reaching and cut across traditional educational boundaries. The project involves novel, inductive-based learning modules, where graduate students team with undergraduate researchers.

Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations. http://swag.engineering.asu.edu/ Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations. http://swag.engineering.asu.edu/ Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations.

This project, investigating active building facades that proactively contribute to energy conservation by changing their opacity and air permeability as a function of environmental and user parameters, promises to contribute strongly to both the cyber and physical sciences. Often energy is wasted when parts of a building are heated or cooled, but are not actually used, or when they are actively cooled if simply opening a window would suffice. The proposed "Self-Organizing Amorphous Facades" (SOAF) consist of a large number of identical cells that can each change their opacity and air permeability, sense light, temperature, and occupancy, and communicate with each other in a distributed collective. For complex cyber physical systems, this promises to provide a novel design methodology that is potentially applicable to a large class of systems and, therefore, will result in foundational knowledge of use to the community at large. This high-risk, high-reward project integrates ideas from computer science and engineering, with a little human physiology and environmental science thrown in, to develop new theoretical foundations for the design, validation, and improvement of coordination strategies for multi-agent robotic systems. The project's intellectual merit lies in novel algorithms that allow one to take advantage of distributed computation to drastically reduce the dimensionality of the data coming from the system, and novel algorithms that turn low-dimensional control data to the system into high-dimensional control signals. In particular, this research focuses on distributed algorithms that can identify regions that share similar spatio-temporal data, distributed algorithms that recognize patterns and gestures in spatio-temporal data sets, and distributed algorithms that automatically derive distributed policies for global control signals on temperature and light. Broader Impacts: The direct impact of this project will be huge potential reduction in the energy footprint of modern buildings by active lighting and ventilation control. A related impact is the introduction of novel ways of using space using truly reconfigurable walls. Due to its interdisciplinary nature spanning computer science and civil engineering together with its positive environment implications, this project is likely to be attractive to students with a broad range of backgrounds and interests. It will lead to educational modules that let students explore energy, heat transfer and solar gains in a building using sensors, wireless technologies, and algorithms, and introduce students to the challenges of complex cyber-physical systems. The PI proposes outreach to women and minorities and suggests a novel mechanism of comic distribution via HowToons.com that will make technical results and environmental impact of CPS accessible to a wide audience.

Buildings in the U.S. contribute to 39% of energy use, consume approximately 70% of the electricity, and account for 39% of CO2 emissions. Hence, developing green building architectures is an extremely critical component in energy sustainability. The investigators will develop a unified analytical approach for green building design that comprehensively manages energy sustainability by taking into account the complex interactions between these systems of systems, providing a high degree of security, agility and robust to extreme events. The project will serve to advance the general science in CPS, help bridge the gap between the cyber and civil infrastructure communities, educate students across different disciplines, include topics in curriculum development, and actively recruit underrepresented minority and undergraduate students. The main thesis of this research is that ad hoc green energy designs are often myopic, not taking into account key interdependencies between subsystems and users, and thus often lead to undesirable solutions. In fact, studies have shown that 28%-35% of LEED-certified buildings consumed more energy than their conventional counterparts, all of which calls for the development of a comprehensive analytical foundation for designing green buildings. In particular, the investigators will focus on three interrelated thrust areas: (i) Integrated energy management for a single-building, where the goal is to jointly consider the complex interactions among building subsystems. The investigators will develop novel control schemes that opportunistically exploit the energy demand elasticity of the building subsystems and adapt to occupancy patterns, human comfort zones, and ambient environments. (ii) Managing multi-building interactions to develop (near) optimal distributed control and coordination schemes that provide performance guarantees. (iii) Designing for anomalous conditions such as extreme weather and malicious attacks, where power grid connections and/or cyber-networks are disrupted. The research will provide directions at developing an analytical foundation and cross-cutting principles that will shed insight on the design and control of not only building systems, but also general CPS systems. An important goal is to help bridge the gap between the networking, controls, and civil infrastructure communities by giving talks and publishing works in all of these forums. The investigators will disseminate the research findings to industry as well as offer education and outreach programs to the K-12 students in STEM disciplines. The investigators will also actively continue their already strong existing efforts in recruiting women and underrepresented minorities, as well as providing rich research experience to undergraduate REU students. This project will provide fertile training for students spanning civil infrastructure research, networking, controls, optimization, and algorithmic development. The investigators will also actively include the outcomes of the research in existing and new courses at both the Ohio State University and Virginia Tech.