MPE 2013+ Workshop on Data-aware Energy Use

September 29 - October 1, 2014
University of California, San Diego

Yuvraj Agarwal, Carnegie Mellon University, Yuvraj.Agarwal at
Mario Berges, Carnegie Mellon University, marioberges at
Midge Cozzens, DIMACS (Education Chair), midgec at
Rajesh Gupta, University of California, San Diego, rgupta at
Zico Kolter, Carnegie Mellon University, zkolter at
Balarkrishnan Narayanaswamy (Murali), University of California, San Diego, muralib at
Fred Roberts, Rutgers University and DIMACS, froberts at
Presented under the auspices of the DIMACS Special Program: Mathematics of Planet Earth 2013+ and the Special Focus on Energy and Algorithms.
With acknowledgement of additional University of California, San Diego support.

Invited Speaker Abstracts:

Yuvraj Agarwal, Carnegie Mellon University,

Title: From Buildings to Smart Buildings

Our societal energy consumption is rising at a staggering rate. Part of this problem is due to the rise in energy use, and in turn the carbon footprint, of Information Technology equipment worldwide (devices, servers, networking equipment). I will briefly describe two energy saving architectures, Somniloquy and SleepServers, that we have developed that enable IT equipment, such as PCs, to be in a hybrid state of operation where they are able to enter sleep modes while maintaining network connectivity thereby saving 70% energy on average.

While computing is indeed part of the problem due to its increasing carbon footprint, in the second part of my talk, I will show that computing is also part of the solution, where it can be used to make other systems much more energy efficient. In particular, I will give an overview of sensing and control solutions that we have designed and deployed within enterprise buildings to make them more energy efficient and sustainable. I will show that by using fine-grained occupancy information gathered either from battery powered wireless sensors, or from smartphones and WiFi Access Points, the energy consumption of the HVAC system within a building can be reduced dramatically, saving up to 40% in a test deployment. Finally, I will describe a new open-source architecture called BuildingDepot that enables development of apps for the "Smart Buildings" of the future and give a few example Apps that we have developed on top of this platform.


Yuvraj Agarwal is an Assistant Professor in the School of Computer Science at Carnegie Mellon University. He completed his PhD from the University of California, San Diego. His research interests are at the intersection of Systems and Networking and Embedded Systems, and he is particularly excited about research problems that benefit from using hardware insights to build scalable and energy efficient systems. In recent years, his work has focused on Green Computing, Mobile Computing and Energy Efficient Buildings. In 2012, he was awarded the "Outstanding Faculty Award for Sustainability" given by the UC San Diego Chancellor. He is a member of the IEEE, ACM and USENIX.

Mario Berges, Carnegie Mellon University

Title: Towards More Automated Engagement of Electrical Appliances in the Smart Grid

Residential and commercial buildings contain a growing number of electrical appliances delivering many of the services that these human shelters offer. Traditionally, information about the past and future electrical power consumption of a large portion of these devices has been undocumented and separated from decisions taken by the building occupants, facility managers and grid operators. However, an increased awareness of the benefits of energy conservation in light of future climate change scenarios, as well as a restructuring of the power infrastructure towards a more interconnected and flexible grid have brought about significant opportunities and use cases for this type of information. Yet, obtaining granular and accurate information about appliance power consumption from existing measurement sources is not straightforward given that our metering infrastructure (and technologies) do not provide full observability. In this talk I will present recent advances my group has made towards providing two pieces of information regarding appliances in buildings, namely: identifying appliance types from measurements of voltage and current at the outlet, and estimating the state of a large population of refrigerators (or any other thermostatically controlled load) from measurements of their aggregate power consumption.


Mario Berges is an assistant professor in the Department of Civil and Environmental Engineering at CMU. He is interested in making use of cost-effective sensor systems to automatically create models and generate insights that can be used to improve the behavior of infrastructure systems, prevent failures, and better plan for the future. He has been working on three different approaches related to this: appliance-level energy feedback through minimally intrusive strategies, sharing sensing and actuation resources at Internet scales, and unsupervised sensor fusion for proactive energy management. His current research interests also include machine learning, signal processing, the Internet of Things and the smart grid. He is the faculty co-director of the IBM Smart Infrastructure Analytics Laboratory at CMU, as well as the director of the Intelligent Infrastructure Research Lab (INFERLab). Among recent awards, he received the Outstanding Early Career Researcher award from FIATECH in 2010. He received his B.Sc. in 2004 from the Instituto Tecnologico de Santo Domingo, in the Dominican Republic; and his M.Sc. and Ph.D. in Civil and Environmental Engineering in 2007 and 2010, respectively, both from Carnegie Mellon University.

A. J. Brush, Microsoft Research

Title: Lab of Things: An extensible platform for Home Sensing, Data Collection, and Actuation

Lab of Things: An extensible platform for Home Sensing, Data Collection, and Actuation To analyze and use data to make better decisions about energy use, we need to collect the appropriate data and act on it. Inspired by challenges deploying data collection and prototype systems into homes, we built the Lab of Things (LoT) platform and released the SDK in July 2013 ( With LoT, our goal is to substantially lower the barrier for researchers and students to develop and experiment with new technologies for the home environment. Using an ongoing EV2Home energy study, where we are collecting data to understand the potential of batteries in electric vehicles to meet the electricity demands of homes, I will illustrate the features of the Lab of Things platform. These include a common framework to write applications that use an extensible set of connected devices (energy meters, motion sensors, lights, cameras, etc) and a set of cloud services that enable remote command/control of devices, monitoring of system health, and robust data collection. I will also highlight how other academics are using Lab of Things for both teaching and research projects, focusing on energy related projects.


A.J. Bernheim Brush is a Senior Researcher at Microsoft Research. A.J.'s research area is Human-Computer Interaction with a focus on Ubiquitous Computing and Computer Supported Collaboration (CSCW). A.J. is most well known for her research on technologies for families and her expertise conducting field studies of technology. Her current focus is home automation as co-leader of the Lab of Things project. She is a Senior Member of the ACM and was honored to receive a Borg Early Career Award in 2010. Her research has received 2 best paper awards and several best paper nominations. A.J. is co-general chair of UbiComp 2014, and serves on the UbiComp Steering Committee and the CRA-W board. A.J. also serves regularly on Program Committees for many conferences including UbiComp, Pervasive, CHI, and CSCW.

Tamra Carpenter, DIMACS

Title: Teach Your Children Well: Planning for Sustainability

In addition to its role in catalyzing and conducting research, DIMACS has a long history of connecting research with education at all levels. In this talk, I will describe our efforts developing and testing instructional modules that bring sustainability topics into high school classrooms. The modules are designed to demonstrate the tools of mathematics and computer science as they are applied to address sustainability topics that students find personally relevant (and perhaps even empowering). The talk will describe the general structure of our module-development projects, how we work with schools, and the content of modules on passive solar building design, buying and operating an electric car, and weather generator models. The talk is not a research talk but is presented in the hopes of inspiring ideas for new modules based on current research topics.


Tamra Carpenter is a Research Professor and Acting Deputy Director of DIMACS (The Center for Discrete Mathematics and Theoretical Computer Science) at Rutgers University. Prior to joining Rutgers, she worked in the telecommunications industry (Bellcore/Telcordia Technologies) where she led a research group whose work focused on network optimization and traffic modeling. She holds a PhD from Princeton University in Operations Research.

David E. Culler, University of California, Berkeley

Title: Taking off in a Universal Energy Information Plane

A central element of the LoCal project was the development of a universal energy information plane to enable cooperative management of loads and supplies to permit deeper penetration of fluctuating renewables. The "simple Measurement and Actuation Profile" (sMAP) allows a wide range of physical assets to present themselves as RESTful web services with metadata integrated with the resource architecture. Buildings were a focus, as they constitute three-quarters of electricity usage, and sMAP provided the building blocks for the development of a Building Operating System and Services (BOSS) to enable novel energy-related applications. In this talk, we explore three off-shoots of sMAP and BOSS. First, automated metadata acquisition and building analytics address the challenge of scaling energy efficiency technique to the millions of legacy commercial buildings far beyond the pace that professional services can achieve. Second, supporting networks of micro-synchrophasers for distribution tier has led to development of a novel multiresolution time-series store. Finally, OpenBAS utilizes a subset of BOSS to provide an open-source building automation system that integrates HVAC, lighting, plug control, and energy analysis, especially with networked thermostats, lights, and appliances.


Friesen Professor of Computer Science, Electrical Engineering and Computer Sciences, University of California, Berkeley

David Irwin, University of Massachusetts, Amherst, Sean Barker, Bowdoin College and Prashant Shenoy

Title: Model-driven Energy Management for Smart Buildings

The proliferation of smart meter deployments has led to significant interest in analyzing building energy use as part of the emerging `smart grid.' As buildings account for nearly 40% of society's energy use, data from smart meters provides signi cant opportunities for both utilities and con- sumers to optimize energy use, minimize waste, and provide insight into how modern homes and devices use energy. Meter data is often dicult to analyze, however, owing to the aggregation of many disparate and complex loads, as well as relatively coarse measurement granularities. At utility scales, analysis is further complicated by the vast quantity of data, which precludes the use of computationally intensive techniques when monitoring hundreds or even thousands of homes.

In this talk/poster, I outline a model-driven approach to analyzing smart meter data that permits both accurate and efficient data analysis. The core of the approach is to empirically char- acterize the energy usage of different types of elemental electric devices, e.g., resistive, inductive, and non-linear, and distill common usage characteristics for each type. We then use these charac- teristics to construct a compact set of models capable of accurately representing the energy usage of the vast of array of electrical devices in existence. After outlining our general modeling approach, we then brie y discuss potential applications that might bene t from its accuracy and eciency, including online tracking of device power usage, non-intrusive identi cation of devices, utility de- mand response capacity estimation, etc.

Rahul Mangharam, University of Pennsylvania

Title: Model-IQ: Low-cost Model Capture, Control Design and Tools for Energy-Efficient Buildings

We discuss three questions regarding the design and implementation of advanced controls for energy efficient buildings. First, a fundamental problem in the design of such closed-loop Cyber-Physical Systems is in accurately capturing the dynamics of the underlying physical system. To provide optimal control, model-based controls require accurate physical plant models. It is hard to analytically establish (a) how data quality from sensors affects model accuracy, and consequently, (b) the effect of model accuracy on the operational cost of model-based controllers. Second, uncoordinated operation of building HVAC systems can result in temporally correlated electricity demand surges or peaks in the building's electricity consumption. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity can result in high electricity costs and expensive system operation. How can we coordinate energy controllers to reduce the aggregate peak power consumption while ensuring that indoor thermal comfort is always maintained? Third, through the lifecycle of a building, the architectural design, construction, controls deployment and operation are done with very little or no co-design. What tools can we develop to help influence how architectural design choices directly affect the operational efficiency of building automation systems, and also facilitate the co-design of high-performance control systems for the specific building model? By co-simulating high-fidelity campus models with advanced controls how can we assist control engineers to recognize problems, eliminate errors and take informed decisions early in the design stage leading to fewer iterations in the building automation development cycle. To chip away at these questions, we present a low-cost model capture methodology, peak power minimization scheduling and control algorithms and a co-simulation toolbox for energy-efficient building design.


Rahul Mangharam is an Associate Professor in the Dept. of Electrical & Systems Engineering and Dept. of Computer & Information Science at the University of Pennsylvania. He directs mLAB: Real-Time & Embedded Systems Lab and xLAB: Experience Design & Technology Lab at Penn. His interests are in real-time scheduling algorithms for networked embedded systems with applications in medical devices, energy efficient buildings, automotive systems and industrial wireless control networks. Rahul received the 2014 IEEE Benjamin Franklin Key Award, 2013 NSF CAREER Award, 2012 Intel Early Faculty Career Award and was selected by the National Academy of Engineering for the 2012 US Frontiers of Engineering.

Brewster McCracken, Pecan Street

Title: 11 Insights Made Possible by High Resolution Disaggregated Data on Residential Electricity Use

For the past five years, Pecan Street has operated one of the nation˘s most data-intensive research trials on residential electricity use, behavioral response and field performance of different measurement devices. Using smart meters, gateway devices and (primarily) CT-collar systems, Pecan Street researchers have measured appliance-level electricity use every minute in hundreds of homes in three states, including measuring generation from rooftop solar panels in nearly 200 homes and home charging in over 70 homes with electric vehicles. In late 2013, Pecan Street began making all of this data available for free in an online searchable database to the academic research community. This presentation lays out 11 preliminary insights drawing on original data from these research trials.


Brewster McCracken is President and CEO of Pecan Street Inc., a research and commercialization institute focused on the utility industry headquartered at The University of Texas. In July 2014, Time described Pecan Street's work as "the most extensive energy-tracking study in U.S. history. . . . That kind of data is unprecedented in the electricity industry. . . The Pecan Street team is already using it to upend long-held theories about electricity use."

In 2013, Smart Grid Today named him one of the nation's "Smart Grid Pioneers", and named him to its VERGE 25 list of 25 U.S. smart grid leaders. He is the author of numerous research reports on customer energy use.

Balakrishnan Narayanaswamy, UC San Diego

Title: Data Driven Investigation of Faults in HVAC Systems

It is estimated that the complexity of modern HVAC systems leads to device mis-configuration in 40% of buildings, and that HVAC faults waste upto 40% of the energy consumed. Fault detection methods used in practice are error prone, with excessive alarms leading to operator alert fatigue, faults left undetected and energy wastage. While sophisticated fault detection techniques have been developed and demonstrated in the literature, they are seldom used in practice. We investigate this gap by applying various fault detection techniques on real data from a 145,000 sqft, five floor building. We find that, while different algorithms are successful in detecting different classes of faults, none of these algorithms capture control loop configuration faults by design. To address this, we develop a novel algorithm, Model, Cluster and Compare (MCC) that is able to detect anomalies by automatically grouping similar entities in an HVAC system, in an unsupervised manner, and com- paring them. We implemented MCC to detect faults in Variable Air Volume (VAV) boxes in our building testbed, and demonstrate that it successfully detects non-obvious configuration faults.

Mary Ann Piette, LBNL

Title: A New Paradigm for Building Data and Control: Linking Internal and External Networks for Greater Energy Efficiency and Grid Integration

This presentation will cover new opportunities, research, and architectures to collect and organize data from both within and outside of buildings to facilitate controls that improve energy efficiency and grid integration. The ultimate goal is to support low-cost, low-carbon, and reliable energy systems. This talk will cover describe trends in new communicating building end-use control systems such as space conditioning, lighting, and plug loads, as well as new concepts to use the existing communication networks in buildings to collect information about occupancy, number of people, location, and prediction of energy use. On the energy supply side there is a growing need to ensure that building loads are flexible and responsive to both local distributed generation and larger electric distribution systems. These data management linked with multi-objective optimal control concepts provide a new paradigm to consider minimizing total energy use, responding to dynamic and time of use pricing, and the availability of demand-side loads to transact with the external systems. The presentation will describe the concept of “resource availability” and the need for end-use systems to predict their ability to be flexible on multiple time scales. The summary will include a discussion about the value proposition associated with these concepts.

Ram Rajagopal, Stanford University

Title: Increasing Consumer Flexibility by Demand-Side Resource Matching

Increase in supply side variability due to increases in renewable generation require demand side management strategies to reduce electricity delivery costs. Smart grid technologies provide opportunities for measuring and controlling loads at an unprecedented scale. Yet, understanding their performance requires accurately capturing how loads respond to specific technologies and how consumer behavior affects such response. Typical demand side management planning and capability studies have been conducted relying on theoretical scenarios of adoption and response. This talk introduces a stochastic optimization and modeling approach utilizing large scale individual level data made available by AMI deployments (smart meters). We present a methodology to model consumer behavior and utilize approximate algorithms to match consumers to potential demand-side technologies and resources. We introduce new performance bounds for the algorithms and test the methodology on a large customer dataset.


Ram Rajagopal is an Assistant Professor of Civil and Environmental Engineering at Stanford University, where he directs the Stanford Sustainable Systems Lab (S3L), focused on large scale monitoring, data analytics and stochastic control for infrastructure networks, in particular energy and transportation. His current research interests in power systems are in integration of renewables, smart distribution systems and demand-side data analytics. Prior to his current position he was a DSP Research Engineer at National Instruments and a Visiting Research Scientist at IBM Research. He holds a Ph.D. in Electrical Engineering and Computer Sciences and an M.A. in Statistics, both from the University of California Berkeley, Masters in Electrical and Computer Engineering from University of Texas, Austin and Bachelors in Electrical Engineering from the Federal University of Rio de Janeiro. He is a recipient of the Powell Foundation Fellowship, Berkeley Regents Fellowship and the Makhoul Conjecture Challenge award. He holds more than 30 patents from his work, and has advised or founded various companies in the fields of sensor networks, power systems and data analytics.

Fred S. Roberts, DIMACS, Rutgers University

Title: Algorithmic Decision Theory and the Smart Grid

Today's decision makers in fields ranging from engineering to medicine to homeland security have available to them remarkable new technologies, huge amounts of information, and the ability to share information at unprecedented speeds and quantities. These tools and resources are particularly relevant to problems at the nexus of energy, environment, and the economy that lie at the heart of the development of the smart grid. The tools and resources will enable better decisions if we can surmount concomitant challenges: The massive amounts of data available are often incomplete or unreliable or distributed and there is great uncertainty in them; interoperating/distributed decision makers and decision-making devices need to be coordinated; many sources of data need to be fused into a good decision, often in a remarkably short time; decisions must be made in dynamic environments based on partial information; there is heightened risk due to extreme consequences of poor decisions; decision makers must understand complex, multi-disciplinary problems. In the face of these new opportunities and challenges, the field of Algorithmic Decision Theory (ADT) aims to exploit algorithmic methods to improve the performance of decision makers (human or automated). ADT is extremely relevant to problems of the smart grid, which allows for real-time precision in operations and control previously unobtainable but raises new issues of privacy and vulnerability.

Bruno Sinopoli, Carnegie Mellon University

Title: On the Security of Cyber-Physical Systems

Cyber Physical Systems (CPS) refer to the embedding of widespread sensing, computation, communication, and control into physical spaces. Application areas are as diverse as aerospace, chemical processes, civil infrastructure, energy, manufacturing and transportation, most of which are safety-critical. The availability of cheap communication technologies such as the internet makes such infrastructures susceptible to cyber security threats, which may affect national security as some of them, such as the power grid, are vital to the normal operation of our society. Any successful attack may significantly hamper the economy, the environment or may even lead to loss of human life. As a result, security is of primary importance to guarantee safe operation of CPS.In an offensive perspective, attacks of this sort can be carried out to disrupt the functionality of the enemy's critical infrastructures without destroying it or even be directly identified. Stuxnet, the malware at the root of the destruction of centrifuges employed to enrich uranium in Iran's nuclear facilities, is a clear example of how strategically important is to gain a deep understanding of CPS security. In this talk I will provide an introduction to CPS security, give an overview of recent results from our research group as well as directions for future work.


Bruno Sinopoli received the Dr. Eng. degree from the University of Padova in 1998 and his M.S. and Ph.D. in Electrical Engineering from the University of California at Berkeley, in 2003 and 2005 respectively. After a postdoctoral position at Stanford University, Dr. Sinopoli joined the faculty at Carnegie Mellon University where he is an associate professor in the Department of Electrical and Computer Engineering with courtesy appointments in Mechanical Engineering and in the Robotics Institute and co-director of the Smart Infrastructure Institute, a research center aimed at advancing innovation in the modeling analysis and design of smart infrastructure. Dr. Sinopoli was awarded the 2006 Eli Jury Award for outstanding research achievement in the areas of systems, communications, control and signal processing at U.C. Berkeley, the 2010 George Tallman Ladd Research Award from Carnegie Mellon University and the NSF Career award in 2010. His research interests include networked embedded control systems, distributed estimation and control with applications to wireless sensor-actuator networks and Cyber-physical systems security.

Mani Srivastava, University of California, Los Angeles

Title: Sensing Infrastructures for Building Analytics

Sensors, by providing high accuracy and resolution observations of energy use, occupancy, and other processes in buildings, enable smarter control of building subsystems for improving overall performability. Drawing upon our experience with sensing systems for building energy use and occupancy analysis deployed at UCLA and IIIT-Delhi as part of NSF’s India-US PC3 program, this talk will examine (i) challenges in scaling the technology across the distinctive operating context presented in India vs. US, (ii) opportunities for lower-cost sensing as well as risks to privacy resulting from ability of opportunistically available sideband signals.


Mani Srivastava is on the faculty at UCLA where he is associated with the Electrical Engineering Department and the Computer Science Department. His research is broadly in the area of networked human-cyber-physical systems, and spans problems across the entire spectrum of applications, architectures, algorithms, and technologies. His current interests include issues of privacy, security, data quality, and variability in the context of applications in mHealth and sustainable buildings.

Adam Wierman, California Institute of Technology

Title: Data centers and energy: Did we get it backwards

The typically story surrounding data centers and energy is an extremely negative one: Data centers are energy hogs. This message is pervasive in both the popular press and academia, and it certainly rings true. However, the view of data centers as energy hogs is too simplistic. The goal of this talk is to highlight that, yes, data centers use a lot of energy, but data centers can also be a huge benefit in terms of integrating renewable energy into the grid and thus play a crucial role in improving the sustainability of our energy landscape. In particular, I will highlight a powerful alternative view: data centers as demand response opportunities.


Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at the California Institute of Technology, where he is a founding member of the Rigorous Systems Research Group (RSRG) and maintains a popular blog called Rigor + Relevance. His research interests center around resource allocation and scheduling decisions in computer systems and services. He received the 2011 ACM SIGMETRICS Rising Star award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been coauthor on papers that received of best paper awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance (twice), IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS.

Short Talk Abstracts:

Muhammad Adnan, University of California, San Diego

Title: Spatio-Temporal Workload Deferral in Data Centers for Energy Efficiency

As computation is outsourced to the Clouds and Mobile platforms, its diversity in the latency and responsiveness needs presents new challenges in scheduling and provides opportunities in energy efficiency. In this research, we explore these opportunities and utilize the flexibility from the Service Level Agreements (SLAs) to differentiate among workloads under bounded latency requirements. We propose a novel approach for cost savings in geographically distributed data centers that takes into account the cost and quality of energy. We devise spatio-temporal deferral techniques for load balancing leveraging dynamic assignment and migration of jobs to the more available and cheaper sources of energy. We validate our algorithms using MapReduce traces from Facebook and show that geographic load balancing with dynamic deferral can provide 20-30% cost-savings compared to the naive greedy solution without deferral. Inside a single data center, we devise capacity provisioning techniques utilizing the flexibility of temporal-deferral to dynamically right-size the data center for energy proportionality. We present online algorithms with bounded competitive ratio and validate them on MapReduce workload by provisioning capacity on a Hadoop cluster that show 20-40% cost-savings compared to the suboptimal follow the workload provisioning. We also investigate the impact of dynamic deferral on user satisfaction. Along with spatiotemporal workload deferral, we devise techniques to capture the loss in value of deferred execution by utility functions and make a trade-off between energy efficiency and user satisfaction. Our simulation on MapReduce traces show that energy consumption can be reduced by 15% with utility-aware deferred load balancing. In this paper, we highlight the key insights of the above three techniques for workload scheduling in data centers and show how energy savings can be achieved via dynamic deferral.

Bharathan Balaji, UCSD

Title: Energy Analysis of HVAC Systems in Commercial Buildings

HVAC systems in modern commercial buildings contribute to 40% of its energy use, and yet its energy consumption is typically monitored using building level meters, providing little insight into its efficiency of operation. We have built an infrastructure of technologies that enables us to get a deeper view into the energy consumption of an HVAC system. We have developed a web service based data storage solution, called BuildingDepot, which helps collect data across different sensors in the building, exposes these data through RESTful APIs, and provides access control. On top of BuildingDepot, we have developed applications which manage and analyze this data both for building occupants and managers. Genie is our occupant management web service, which provides an interface to the occupants through which they can view room temperature, send comfort complaints and energy consumed by HVAC. BDSherlock is our building manager level web service, that provides an overview of building status, sensor data across the buildings, and identifies the faults that cause energy wastage.

Madhur Behl, University of Pennsylvania

Title: Data-driven Building Model Capture for Economic Energy Demand Control

One of the biggest challenges in the design of closed-loop Cyber-Physical Systems (CPS) is in accurately capturing the dynamics of the underlying physical system. In the context of buildings, the modeling difficulty arises due to the fact that each building is designed and used in a different way and therefore, it has to be uniquely modeled (Heterogeneity). Furthermore, each building system consists of a large number of interconnected subsys- tems which interact in a complex manner and are subjected to time varying environmental conditions (Complexity).

Alex Beltran, UC Merced

Title: Optimal HVAC Building Control with Occupancy Prediction

Buildings account for about 41% of primary energy consumption and 75% of the electricity, 44% more than the transportation sector and 36% more than the industrial sector. Space heating, space cooling, and ventilation are the dominant end uses, accounting for 41% of all energy consumed in the buildings sector. Growing interest in sustainability has resulted in research efforts to reduce energy consumption while providing adequate quality of service to its occupants.

In this work, we present a Model Predictive Control (MPC) framework for optimal HVAC control that minimizes energy consumption while staying within the comfort bounds of the occupants. The novelty of our approach lies in the use of prediction occupancy models derived from data traces and incorporating those models within the MPC framework. We use a Blended Markov Chain (BMC) occupancy prediction model in order to predict thermal load and occpancy of each zone in the building. We test our approach in simulation and compare it with occupancy schedules and control rules currently use in our university buildings. Our preliminary results show that 18% savings in cooling in the summer, and 10\% savings in heating in the winter are achievable when conditioning the building using our MPC/BMC control framework.

Subhonmesh Bose, Cornell University

Title: Placement and Control of Energy Storage in the Grid: a System Operator's Viewpoint

Storage technologies have many potential applications in the power grid ranging from mitigating intermittency of renewable generation to load shifting. In this talk, we consider storage as a communal asset, i.e., the system operator solves the problem of minimizing total generation cost in the grid subject to transmission constraints of the network, when it has access to distributed energy storage resources. Given a stochastic net-demand (load minus renewable supply) process evolving over a transmission-constrained power network, we show that the expected system benefit of storage derived under the optimal dispatch policy is concave and non-decreasing in both energy and power capacities of storage. Thus, the greatest marginal benefit is derived at small energy capacities of installed storage. For such capacities, we characterize the locational (nodal) marginal value of storage in terms of the spectral properties of the stochastic net-demand process. This allows us to calculate the marginal benefits in terms of strict level crossings of a scalar random process derived from wind and demand data across a power network. We present the results of numerical simulations on some power networks. In a second study, we consider the use of storage in load shifting, i.e., attening generation profile to reduce a convex cost of generation. We show how data driven empirical study leads us to an interesting observation, which we characterize (partially) in a structural result: a generator node that connects to the rest of the network through one capacity-constrained transmission line never needs any storage capacity under optimal allocation. This holds regardless of the demand profile or other network parameters.

Joyati Debnath, Winona State University

Title: Issues related to Energy Consumption in STEM Education

The use of energy is a global issue and can be addressed via mathematics courses. Equity in the consumption, consumerism and distribution of available energy is an important global issue for the students to understand. It is known worldwide that STEM (Science, Technology, Engineering and Mathematics) Education is critical in an effort to prepare our students and promote innovation, creativity, global competence, democratic principles and social justice, improve economic competitiveness, and provide leadership. It has been stated that the poorest 10% only accounts for 0.5% and the wealthiest 10% accounts for 59% of all consumption. Globally, 20% of the world's population in the higher-income countries accounts for 86% of total private consumptions and the 20% of world's population in the lower-income countries accounts for 1.3% of total private consumption. The goal of this project is to present with a design, create and develop academic content in a course related to energy consumption in accordance with students' level of mathematical understanding, and then implement and assess the outcomes

Zhichuan Huang, University of Maryland

Title: Minimizing Data Storage for High Granularity Energy Data Collection

To reduce peak demand, many utility companies are transitioning from fixed rate pricing plans to real-time pricing plans. To apply real-time pricing plans, it is crucial to collect accurate energy data with high granularity from individual homes. Thus, smart meters that measure energy data in high time granularity have been rolled out in many countries. However, energy data with high granularity needs large data storage and would generate significant communication overhead for utility companies to collect all the data from large amount of homes. In this paper, we present a frame work for utility companies to collect energy data with minimum data storage, computing resources and communication overhead. We first make use of a polynomial curve to sketch the approximate curve of power consumption. Then based on analyses of power consumption patterns, we propose two different types of methods to record the differences between real power consumption curve and the polynomial curve with minimum storage. We conducted extensive system evaluations with 30 homes' power consumption data every second for more than 2 months. Results indicate i) our design can reduce data storage significantly by 70% with only 0.02kW error on average from real data in a single home, and ii) even though our design is based on energy consumption data, it also works well with other energy data (e.g., voltage and frequency).

Srinivasan Iyengar, University of Massachusetts, Amherst

Title: SolarCast: An Open Service to Predict Solar Power in the Black Box Setting

With reducing costs, homeowners are encouraged to install rooftop solar generation units. But solar power is characteristically intermittent and inconsistent due to the diurnal nature of sun and varying weather conditions at any given place. Thus, predicting solar power is non-trivial. Presence of a solar power prediction service would help in both supply and demand side load management by integrating with smart devices, sunny load scheduling, predicting grid level variable aggregated demand, designing incentives to customers with distributed power generations capacity etc. Past work, has primarily focused on white-box prediction schemes that require detailed site and panel specific information. A homeowner may not always have such information at his disposal. Further, in case of rooftop installations there is an additional challenge of shadows caused by local topology. We present SolarCast, an open service to make black box solar power predictions. It only requires a geographic location of the site and a minimal amount of historical generation data. We evaluate SolarCast's accuracy on a geographically diverse dataset of solar deployments across the different states in US. We also expose RESTful APIs for developers to integrate our predictions into their smart energy devices and other related apps. With increased adoption of our service, we intend to evolve more powerful models that would improve our prediction accuracy.

Amanpreet Kaur, UCSD

Title: Net load forecasting for communities with distributed and centralized solar generation

The power grid is undergoing a change. The growth of solar power utilization has been staggering: global solar capacity has increased from 1.76 GW to 102.15 GW over the past decade (2001-2012). Due to the stochastic nature of solar power, these increasing levels of solar penetration result in several planning and operational challenges. The impact of an increasing solar penetration on load forecast capability was studied for a community with centralized solar generation and 33% annual solar penetration. It was found that the load forecasting skill drops by 9% for the hour-ahead load forecast because of additional variability and uncertainty introduced by solar power. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. Therefore, net load forecasting i.e., the integration of demand load and renewable generation forecast techniques is needed to replace existing grid load management systems. Net load forecasting models for communities with centralized and distributed generation are being developed. Preliminary results show that the accuracy of net load forecast is limited by the solar forecasting capabilities. For systems with centralized solar generation, the ramps in net load are proportional to solar irradiance whereas for distributed system, ramps are related to weighted average of solar power. Lastly, for systems with distributed solar generation and high solar penetration (>5%), the error magnitude is a linear function of solar variability. Thus, while allocating resources and operating reserves for grids with high solar penetration, the forecasted solar variability of the next day should also be considered.

Zhenhua Liu, LBNL & Stony Brook University

Title: Optimizing Demand Response for Smart Grid

Demand response is widely recognized as a crucial tool for incorporating renewables into the grid. This talk presents my research efforts towards a rigorous mathematical foundation for optimizing demand response for smart grid. This is an interdisciplinary challenge for both control algorithm design for local customers and market design for utility companies and society. For local control algorithm design, I will first focus on data centers, which represents the fastest growing sectors in energy usage and greenhouse gas pollution. I have designed algorithms with theoretically provable guarantees to deal with information uncertainties and the need of distributed control. Together with Southern California Edison, we quantified the potential of data center demand response and showed that it's worth billions of dollars worldwide. Moving from theory to practice, I helped HP design and implement the industry's first Net-Zero Energy Data Center. I will then briefly discuss other demand response opportunities in buildings and electric vehicles, based on my recent work at Lawrence Berkeley National Lab. In order to make sure the demand response provided by local customers aligned well with the global objective of utility companies and society, we need to design electricity market in the right way to incentivize customers. I will present our preliminary results along this line.

Enrique Mallada, Caltech

Title: Fair Load-side Control for Frequency Regulation in Smart Grids

Frequency control rebalances supply and demand while maintaining the network state within operational margins. It is implemented using fast ramping reserves that are expensive and non-renewable, and which are expected to grow with the increasing penetration of renewables. The most promising solution to this problem is the use of demand response, i.e. load participation in frequency control. Yet it is still unclear how to efficiently integrate load participation without introducing instabilities and violating operational constraints.

In this talk we present a comprehensive load-side frequency control mechanism that can maintain the grid within operational constraints. Our controllers can rebal- ance supply and demand after disturbances, restore the frequency to its nominal value and preserve inter-area power ows. Moreover, our controllers are distributed (unlike generation-side), fair among participating loads, and can further maintain line ows within thermal limits. We prove that such a distributed load-side control is globally asymptotically stable and illustrate its convergence with simulation

Leandro Soriano Marcolino, University of Southern California, Los Angeles

Title: Agents Vote for the Environment: Designing Energy-Efficient Architecture

In this research-in-progress paper we present a new real world domain for studying the aggregation of different opinions: early stage architectural design of buildings. This is an important real world application, not only because building design and construction is one of the world's largest industries measured by global expenditures, but also because the early stage design decision making has a significant impact on the energy consumption of buildings. We present a mapping between the domain of architecture and engineering research and that of the agent models present in the literature. We study the importance of forming diverse teams when aggregating the opinions of different agents for architectural design, and also the effect of having agents optimizing for different factors of a multi-objective optimization design problem. We find that a diverse team of agents is able to provide a higher number of top ranked solutions for the early stage designer to choose from. Finally, we present the next steps for a deeper exploration of our questions.

Dung Nguyen, University of California, San Diego

Title: Integration Strategies for High Penetration of Photovoltaics Using Solar Forecasting, Control, and Optimization

As solar energy penetration in the power grid increases, utilities face great challenges coping with the variable output of solar power, especially from photovoltaics (PV) panels. To increase solar energy penetration without compromising grid operating cost and compliance with voltage standards and to maximize economic benefits of PV systems, a complex system design and control of PV inverters, energy storage systems, and existing power line components such as voltage regulators is required. The impact of solar variability on distribution feeders with high solar energy penetration is examined via quasi steady-state simulations, coupled with solar now casting using high resolution sky imager, at different solar penetration levels and cloud conditions. The adverse impacts of variable solar power include voltage fluctuations, high frequency and magnitude power ramps, and increasing tap operations. Several control schemes utilizing PV inverters, storage systems, and solar forecasting are investigated to mitigate these impacts and maximize the benefits of solar energy. Some specific approaches to be studied are: 1) local control of single inverter unit (in PV and/or ESS) for local voltage stabilization and ramp smoothing using active and reactive power compensation with solar forecasting in sub-minute time scale; 2) distributed model predictive control of real and reactive power from PV inverters and storage systems for line loss minimization, cost reduction of power provided by utilities, and ramp mitigation; 3) centralized control of real and reactive power from multiple distributed resources for global ramp rate mitigation, global voltage stabilization, reduction of tap operations and overall generation cost minimization. PV and load hosting capacity of the distribution feeder will be improved when these controls are applied appropriately. The tools used for this study are OpenDSS for quasi-steady state simulation and Matlab for data processing, control scheme design, and optimization.

Wentao Ouyang, UCLA

Title: Beyond Binary Occupancy: Non-Intrusive Sensing of Occupancy Level and Occupant Identities

Information about the occupancy status in residential and commercial buildings is a key enabler for a number of energy efficiency and automation applications. Current systems for occupancy inference usually require the installation of dedicated sensors such as motion sensors, acoustic sensors, or cameras. In this paper, we investigate the feasibility of using digital electricity meters as an opportunistic sensor for non-intrusive sensing of fine-grained occupancy status. Our intuition is that since different occupants usually operate on different devices with their own power signatures, different patterns in the aggregate electricity consumption will reflect the number and identities of current occupants. We have collected data in a three-person family home and a twelve-person university lab, and performed an in-depth analysis of the correlation between the electricity consumption pattern and occupancy status. Our experimental results demonstrate that by leveraging the electricity consumption data, we can infer not only whether a space is occupied, but also the number and identities of occupants with sufficient accuracy, significantly better than a naive strategy that does not use such sensory data.

Co-authored with Kevin Ting and Mani Srivastava.

Nathan Pollesch, University of Tennessee - Knoxville

Title: Bioenergy Sustainability Assessment: Mathematical approaches to protocol developmen

Given the diversity of production pathways and as well as the interest in bioenergy as a renewable energy resource on a global scale, bioenergy sustainability assessment methodologies must be developed that are adaptable for assessing diverse production methods and flexible to support the range of analyses that researchers and policymakers may seek to utilize with them. These assessment goals must also be reached while maintaining mathematical rigor with respect to aggregation, normalization, and quantifying data uncertainty. This talk presents indicators for bioenergy sustainability and discusses how the study of aggregation functions can be used to address challenges in the development of bioenergy sustainability assessment protocols.

Borhan Sanandaji, University of California, Berkeley

Title: Exploiting Flexible Loads for Renewable Integration

Vast and deep integration of renewable energy resources into the existing power grid is essential in achieving the envisioned sustainable energy future. Many countries around the globe as well as many states in the U.S. have set up aggressive Renewable Portfolio Standards (RPSs). The state of California, as an example, has targeted a 33% RPS by 2020. Volatility, stochasticity, and intermittency characteristics of renewable energies, however, present a challenge for integrating these resources into the existing grid in a large scale as the proper functioning of an electric grid requires an instantaneous and continuous power balance between supply and demand. In this talk, we see how the demand-side exibility can be used to match random supply. In particular, we show that the thermal storage potential of a collection of Thermostatically Controlled Loads (TCLs) (such as residential air conditioners) can be modeled as a battery and can be leveraged for enabling a deep penetration of renewable energy resources.

Bochao Shen, Northeastern University

Title: SmartShift: Expanded Load Shifting Incentive Mechanism for Risk-averse Consumers

Peak demand for electricity continues to surge around the world. The supply-demand imbalance manifests itself in the form of rolling brownouts in California and power cuts in India. Exposing consumers to real-time pricing, in an effort to incentivize them to shift their usage pattern, is often suggested as an approach to mitigate this problem. This is akin to increasing tolls at peak commute times. We show that risk-averse con- sumers of electricity react to price fluctuations by scaling back on their total demand, not just their peak demand leading to the unintended con- sequence of an overall decrease in production/consumption and reduced economic efficiency. We propose a new scheme that allows homes to move their demands from peak hours in exchange for greater electricity con- sumption in non-peak hours. This is akin to how airlines incentivize a passenger to move from an over-booked flight in exchange for, say, two tickets in the future. We present a formal framework for the incentive model that is applicable to different forms of the electricity market. We show that our scheme not only enables increased consumption and con- sumer social welfare but also allows the distribution company to increase pro ts. This is achieved by allowing load to be shifted while insulating consumers from real-time price fluctuations. This win-win is important if these methods are to be incorporated in practice.

Nicole Sintov, ISI at USC

Title: Using Behavioral Science to Make an Impact with Energy Data in the Field

Smart Grid systems across the world are generating an abundance of energy data that can be leveraged at many levels to make more informed decisions about grid operations. End-users tend to be overlooked, but they are key players in the grid, and engaging them with technologies that make use of this data is critical. Hence, an optimally functioning grid couples new technology with changes in human behavior. Although grid technologies themselves vary considerably, they generally share common goals: to reduce energy consumption via efficiency and/or curtailment, to shift use to off-peak times of day, and to enable distributed storage and generation options. End-users are integral to unlocking these potentials for impact. In this presentation, we highlight several ways in which behavioral science can be applied to better understand and engage customers in smart grid systems.


Nicole Sintov holds a Ph.D. in Psychology from USC. She is a Behavioral Scientist at the USC Information Sciences Institute and teaches courses in the USC Department of Psychology and USC Environmental Studies Program. Dr. Sintov's research focuses the development of technologies that can be used to modify human behavior in order to promote environmentally beneficial outcomes, particularly related to reducing energy consumption among end-users. Incorporating behavioral science principles such as persuasion, social influence, and goal theory, her work aims to increase adoption of smart grid technologies and achieve energy conservation and load-shifting through field studies. She has deployed numerous such community-based field programs that directly engage end-users by combining energy data with tools of behavior change. She is the recipient of a grant to investigate Customer-Focused Smart Grid Technologies from the Los Angeles Department of Water and Power and also is Co-PI of Dept. of Defense MURI award for Scalable, Stochastic and Spatiotemporal Game Theory for Real-World Human Adversarial Behavior.

Daniel Winkler, California Institute of Technology

Title: Distributed Independent Actuation for Irrigation Control

In this work, we propose that irrigation systems with distributed independent actuation can substantially reduce water consumption in lawn irrigation. To test this theory, we develop a computationally light fluid flow model that allows the optimization of valve scheduling using standard optimization techniques. With these optimized valve schedules, we then show in simulation that water savings up to 64% are possible over current ad-hoc watering schedules on our university's lawns. Extended to all irrigated space on our campus, these savings reflect a reduction of approximately 20 M gallons annually, a savings of $112,000.

Vahraz Zamani, University of California, San Diego

Title: AMI data for Advanced Distribution Automation

More than 60% precent of Smart Grid Investment Grant (SGIG) projects involve to install the Advanced Metering Infrastructure (AMI). The main purposes for AMI installation are to support the billing procedure, customer services and to improve the operational efficiencies. AMI gathers load data at customers level, i.e. household level.

Customer load profiles show more stochastic behavior by decreasing the time scale as well as by integration of rooftop PV panels. By considering the scale of data variations in time and space, i.e. spatio-temporal data, households, data-driven methods are needed to provide the actionable information for power system operation at utility scale.

This presentation will focus on developing statistical solutions to construct the load models at distribution feeders to improve systems operations and load estimation at distribution transformers for advanced distribution automation programs, e.g. Conservation Voltage Reduction (CVR).

Baosen Zhang, Stanford University

Title: Competition and Coalition Formation of Renewable Power Producers

We investigate group formations and strategic behaviors of renewable power producers in electricity markets. These producers currently bid into the day-ahead market in a conservative fashion because of the real-time risk associated with not meeting their bid amount. It has been suggested in the literature that producers would bid less conservatively if they can form large groups to take advantages of spatial diversity to reduce the uncertainty in their aggregate output. Using real world bid data from system operators, we show that large groups of renewable producers would act strategically to lower the aggregate output because of market power. To maximize renewable power production, we characterize the trade-off between market power and generation uncertainty as a function of the size of the groups. We quantify a sweet spot in the sense that there exists groups that are large enough to achieve the uncertainty reduction of the grand coalition, but are small enough such that they have no significant market power. We consider both independent and correlated forecast errors under a fixed real-time penalty. We also consider a real-time market where both selling and buying of energy are allowed. We validate our claims using PJM and NREL data.

Demo Abstracts:

Natasha Balac, San Diego Supercomputing Center (SDSC)

Title: UCSD Microgrid PI system: Leveraging large data in a campus microgrid

University of California, San Diego (UCSD) is an owner-operator of a 45 MW peak-load smart grid with multiple renewable and non-renewable energy generation resources, significant energy storage capacity and sophisticated monitoring and control of flex-demand loads. It sustains a community of 54,000 residents who have increasingly sophisticated and growing energy needs. This project harnesses over 85,000 smart-grid data points and all relevant novel-data with advanced forecasting and analytics in order to realize potential gains in energy efficiency and reductions in energy cost by utilizing fine grain Key Performance Indicator (KPI) analysis. These KPI are used to analyze building performance by calculating values that measure the efficiency of the building which can include energy usage and level of comfort information. This Demo outlines the development of a new method of using existing data from HVAC systems to provide facilities managers with actionable information from which proactive and preventative maintenance can be performed. The system detects abnormal zone behavior and automatically sends email notifications to facilities management. The system uses an object oriented approach to developing meta-data models of rooms, zones, floors and building electric and thermal power. The meta-data structures are used to compute key performance indicators for use in comparative analyses. An automated room agent determines the condition of each room in each building and automatically alerts facilities management of abnormalities. Unacknowledged alerts are automatically escalated. The results of the building comparative analyses are displayed on a publically accessible website providing building occupants with real time performance measures of buildings on campus. The goal of the project is to promote responsible, data-driven individual choices for maximizing renewable energy use within daily activities.

Kleissl Lab, University of California, San Diego

Title: Short Term Solar Forecasting Using Sky Imagery and Its Potential Application in Control and Optimization for a Smart Grid

The variability and uncertainty of solar generation compared to more conventional fossil power sources creates a unique challenge with respect to the integration of solar resources, requiring larger regulation and reserve capacities to meet ancillary service requirements. Of particular interest to the energy industry are sudden and widespread changes in irradiance typically caused by large clouds or widespread changes in cloud cover. Reduction in the uncertainty of solar generation through accurate solar forecasting reduces solar integration costs. This demo presents a short-term solar irradiance forecast technique using a whole sky imager developed at UC San Diego. First, the forecast procedure will be presented and the performance of the system will be validated against distributed ground-based GHI data. Following that, a historical forecast and a real-time operational forecast will be demonstrated. Finally, a potential application of the forecast for control and optimization of a smart grid is shown.

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Document last modified on September 25, 2014.