Information-Based Building Energy Management SEEDM Breakout Session #4.

Slides:



Advertisements
Similar presentations
June Intelligently Connecting Plug-In Vehicles & the Grid.
Advertisements

2006/12/05ICS Home Automation Examples of WSN: (iPower: An Energy Conservation System for Intelligent Buildings) Yu-Chee Tseng (appeared in ICS 2006)
NEMA. 2 Utility Metering vs. Submetering 10/11/2014 New PowerPoint Template Utility Meter Meter provided by utility with the purpose of measuring.
Introduction Build and impact metric data provided by the SGIG recipients convey the type and extent of technology deployment, as well as its effect on.
VSE Corporation Proprietary Information
VPP: Converting Capacity in the Home into Valuable Energy Reserves Wayne Callender.
Presented by: Sheekha Khetan. Mobile Crowdsensing - individuals with sensing and computing devices collectively share information to measure and map phenomena.
Home Area Networks …Expect More Mohan Wanchoo Jasmine Systems, Inc.
4.1.5 System Management Background What is in System Management Resource control and scheduling Booting, reconfiguration, defining limits for resource.
Building Technologies Program. FLEXLAB Background LBNL responded to a 2009 RFP for ARRA funds to develop a facility that: o Develops new test methods.
How ICT can Create a Leaner and More Sustainable Estate
Cyber-Physical Energy Systems: Focus on Smart Buildings Presentation by Francis Usher CS 525 Fall 2012 Monday September 19, 2012.
“Smart Grid” State-of-the-Art: Phasors, Meters, DR, ISO, Markets David Culler, Randy H. Katz, Seth Sanders University of California, Berkeley LoCal 0 th.
LoCal: Rethinking the Energy Infrastructure using Internet Design Principles David Culler University of California, Berkeley Renewable Energy Microgrid.
2006/12/05ICS iPower: An Energy Conservation System for Intelligent Buildings by Wireless Sensor Networks Yu-Chee Tseng, You-Chiun Wang, and Lun-Wu.
Axis Intelligent Video Intelligence where you need it.
 Solar energy is energy that is derived from the sun rays.The light and the heat provided by the sun is “collected” or harnessed by solar panels and.
ICT AND SMART GRIDS GERARD J.M. SMIT CHAIR CAES (COMPUTER ARCHITECTURES FOR EMBEDDED SYSTEMS)
“Collaborative automation: water network and the virtual market of energy”, an example of Operational Efficiency improvement through Analytics Stockholm,
Ambulation : a tool for monitoring mobility over time using mobile phones Computational Science and Engineering, CSE '09. International Conference.
INTERNET OF THINGS Challenges of 21 st century and Technological innovation February -2011
Future of Smart Metering Kansas Renewable Energy & Energy Efficiency Conference September 26, 2007.
IBM Research © 2006 IBM Corporation HARMONI: Client Middleware for Long-Term, Continuous, Remote Health Monitoring Iqbal Mohomed, Maria Ebling, William.
APC InfraStruxure TM Central Smart Plug-In for HP Operations Manager Manage Power, Cooling, Security, Environment, Rack Access and Physical Layer Infrastructure.
Smart Phone Laboratory ECEN 489 Srinivas Shakkottai.
Copyright 2010 – Johnson Controls, Inc. 1 A Day in the Life of a Smart Campus Clay Nesler VP, Global Energy & Sustainability Johnson Controls
CONFIDENTIAL 1. 2 Designing the Intelligent Energy Gateway 2009 CONFIDENTIAL.
Smart Metering and the Smart Grid How does it work and what can it do? Will Chaney 1Energy Awareness Week, 3-8 May 2010.
© 2010 IBM Corporation IBM InfoSphere Streams Enabling a smarter planet Roger Rea InfoSphere Streams Product Manager Sept 15, 2010.
An Overview of the Smart Grid David K. Owens Chair, AABE Legislative Issues and Public Policy Committee AABE Smart Grid Working Group Webinar September.
Windmill by GP from The Noun Project Light Bulb by Nathan Thomson from The Noun Project.
Sensor Database System Sultan Alhazmi
Listen to Your Meters! Pradeep Murthy SunTec Business Solutions.
Martin Schulz Center for Applied Scientific Computing Lawrence Livermore National Laboratory Lawrence Livermore National Laboratory, P. O. Box 808, Livermore,
IPower: An Energy Conservation System for Intelligent Buildings International Journal of Sensor Networks Yu-Chee Tseng, You-Chiun Wang, and Lun- Wu Yeh.
PowerOneData’s GENII Leverages Cloud Platform to Deliver Affordable, Scalable, and Accessible Meter Data Management Software to Customers COMPANY PROFILE:
JEMMA: an open platform for a connected Smart Grid Gateway GRUPPO TELECOM ITALIA MAS2TERING Smart Grid Workshop Brussels, September Strategy &
MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Big Events Hans-Arno Jacobsen Middleware Systems Research Group MSRG.org.
Introduction to Energy Management. Week/Lesson 12 Advanced Technology for Effective Facility Control.
SMART GRID A smart grid for intelligent energy use. By: Suhani Gupta.
+ Logentries Is a Real-Time Log Analytics Service for Aggregating, Analyzing, and Alerting on Log Data from Microsoft Azure Apps and Systems MICROSOFT.
REU 2009 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Information Processing.
Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN.
Smart Grid & Electric Vehicle in Computer Scientist’s Perspective by Minho Shin, Myongji University.
Big Data Javad Azimi May First of All… Sorry about the language  Feel free to ask any question Please share similar experiences.
Information Distribution Connected infrastructure enabling free flow of useful information Assimilation & Distribution Methods of delivery Mobile phone.
Aggregated Energy Data Community Planning December 16, 2015.
Preliminary Design Review Team 18 October XX, 2015 Department of Electrical and Computer EngineeringAdvisor: Csaba Andras Moritz.
A smart grid delivers electricity from suppliers to consumers using two-way digital technology to control appliances at consumers' homes to save energy,
Energy Management Solution
Connected Infrastructure
Makes Insurance Smarter.
Connected Living Connected Living What to look for Architecture
Proponent Group: NEC Solution Innovators, Ltd
Smart thermostat.
IoT at the Edge Technical guidance deck.
Connected Living Connected Living What to look for Architecture
Breakout Session on Smart Grid Data Analytics
Connected Infrastructure
Energy Management Solution
IoT at the Edge Technical guidance deck.
ISMB – Smart Energy activities
Predicting Miscellaneous Electrical Loads (MELs) in Commercial Buildings: A Time Series Analysis Presented by: Behzad Esmaeili, Ph.D. April 26th, 2018.
9.2 The Power of Electricity
Microsoft Azure Enables Big-Data-as-a-Service Applications for Industry and Government Use “Microsoft Azure is the most innovative and robust suite of.
Speaker: Jin-Wei Lin Advisor: Dr. Ho-Ting Wu
ISMB – Smart Energy activities
Bluetooth technology and Smart Energy
GREEN TECHNOLOGY NABCEP RESNET Certification.
Overview: Chapter 2 Localization and Tracking
Presentation transcript:

Information-Based Building Energy Management SEEDM Breakout Session #4

Outline Example future capabilities Observe: data collection Analyze: data analysis Act: decisions and actions Test bed for smart building research 2

Example Future Capabilities Visualize energy usage  Breakdown energy usage for a building  Instead of a single energy bill with an aggregate reading  Compare energy usages across similar buildings  This building is at 90% percentile energy efficiency  Detect abnormal energy usage Context-aware energy analysis, suggestions, actions  Context-aware energy usage profiles  Actionable suggestions to save energy  Automatic actions? Analysis for neighborhood, municipal, etc.  Community-oriented energy savings  Planning purpose  Prediction, optimization 3

Data Collection (Observe) Fine-grain instrumentation:  Power usage + environment & human activities  Multiple types of devices:  Wireless plug meters  Smartphones w/ bluetooth  Multiple targets:  Light, heating, air conditioning, plug load Communication of data  Privacy-preserving  Energy efficiency  Filtering, aggregation, compression 4

Data Analysis What data?  Real-time streams vs. archives  Privacy vs. details Where to run the analysis?  Cloud vs. locally in the building (smart phones? Game console? sensors?) What kind of analysis?  Event detection  Signal processing  Per-house analysis  Aggregation  Abnormal behavior detection How to model? Per-user per-context profiles? How to efficiently support the processing?  Scalability, energy efficiency How to support a large number of users? A common platform?  Many end (home) users, planners 5

Actions Schedule energy usage  E.g. electric vehicle, washing machine Capacity planning Peer-to-peer energy sharing  Solar panel, renewable energy Partnering and understanding the possible actions  Flexible data management for supporting the actions 6

Test Bed DOE:  Energy audit for typical building types  Data are available  But mainly coarse-grain Energy lab / green lab  Modeling after PlanetLab 7

Challenges Data query frameworks Enable variety of action processing Scale-up + out Energy efficiency Privacy 8