MaxTech & Beyond: Design Competition for Ultra-Low-Energy Use Appliances & Equipment

User-Centric Model-Predictive Lighting Retrofit System: 2012-2013. The BEST Lab is a finalist in the MaxTech & Beyond Design competition. MAX TECH & BEYOND “promotes the rapid development of energy efficient appliances and supports the education of the next generation of U.S. clean energy engineers”.The final competition will be held on Thursday, May 23. Prior research in the Berkeley Energy and Sustainable Technologies Laboratory (BEST Lab) has demonstrated that retrofitting zonal lighting control systems with wirelessly actuated, user-controlled, individually dimmable luminaires in offices can save 50% of the office’s electricity usage; with additional daylight controls, it is theoretically possible to save 70% of the energy use. The current best-on-market technology was developed as a parallel effort to our research and reports similar energy savings. As of 2010, 70% of the U.S. stock of commercial buildings does not have intelligent lighting (including new buildings). Fifty percent of the intelligent lighting control systems have been deactivated by the users, and the remaining 50% operate at 50% of target. However, adoption and operational efficiency of smart lighting is adversely affected by high retrofit costs and lack of a closed-loop monitoring system.

Prototype/Technology Description: “User-centric model-predictive lighting retrofit system” is an innovative approach to address the current deficiencies in intelligent lighting retrofits with a comprehensive systemic approach that also adds novel capabilities to the retrofitted system. Our primary contribution is in the development of a new framework/method for retrofitting that includes a combination of new hardware and software components. The package will consist of a wireless illuminance sensor network, wireless luminary controllers, shades with built-in wireless shading controllers, and a server loaded with automated model calibration algorithms, inverse model generation, and predictive control and energy evaluation software with optimization capabilities. The energy and cost savings prediction algorithm will account for operational uncertainties. Users will initially input their lighting preferences manually and will undergo occasional, short satisfaction surveys to adjust dynamically the lighting preferences. The system will use the predictive inverse model to automatically optimize lighting schemes for energy efficiency while maintaining user preferences.

Expanding NASA’s Capacity in Wireless Sensor Networks: Smart Buildings & Space Exploration

Objectives: The objective of this research is to leverage wireless sensor and actuator network technologies to implement green lighting optimization in a test bed in NASA Ames Research Center’s Sustainability Base. This implementation will be used to fill-in-the-gaps in existing energy saving technologies in the Sustainability Base, as well as build a platform for energy conservation research and wireless sensor networks for space exploration. Wireless sensor networks, along with sensor fusion for prognostics, diagnostics and failure recovery, are critical for space exploration and environmental/machine monitoring. We are working with NASA Ames in building a domestic test bed at the Sustainability Base, along with applications to tensegrity space robots. The proposed test bed leverages the versatility of wireless sensor and actuator network technologies, to create a wireless networked lighting system for the green building that accounts for both energy efficiency and user satisfaction. This actuator/ sensor network platform can then be extended to new domains such as space vehicles or space-habitats.

Deliverables: Implement a “plug and play” wireless sensor and actuator network in NASA Ames Sustainability Base and the 4th floor of the CITRIS (Center for Information Technologies in the Interest of Society) building that will allow for research on greater energy savings and increased user satisfaction. The synergy between the new technologies in the Ames Sustainability Base and the existing research at University of California will give a platform for conducting cutting edge research in green technologies and sensor fusion that support the goals of both NASA and UC. Lessons learned from this research platform will be used to make recommendations for a commercial implementation. Our plug and play sensor network is almost ready to be deployed and sample of our sensor readings are now available on the latest version of sMAP. Click on autoupdate to see the latest data in lux which is the unit of light intensity. The data is recorded in CITRIS and conmmunicated at 10 minutes interval from a low power Telosb sensor node.

Smart Lighting – 4th Floor of Sutdarja Dai Hall

Sensor Data from 4th floor of CITRIS: http://best.berkeley.edu/~smartlighting/node_list.html

SMAP data: http://new.openbms.org/plot/?streamids=1b206a39-7481-5972-a9d7-0b75692e6394&start=1335387677000&end=1335388157000&stack=false&tree=0&axes=1

SMAP data of N232: sensor 1, sensor 2, sensor 3, sensor 4, sensor 5, sensor 6, sensor 7, sensor 8, sensor 9. Heatmap at: http://128.102.241.67/UDM/map3.php

CITRIS 4th Floor Lighting Control (green millennium)l: http://green.millennium.berkeley.edu/power/SutardjaDaiHall/Floor4

or Fiat Lux (beta): http://green.millennium.berkeley.edu/power/SutardjaDaiHall/Floor4

 

Greening The Internet of Things: Smart Products in a Smart Grid

New Initiative: Every electronic appliance or device has the potential now to be a node in the Smart Grid, yet most of these appliances and the built environments that house them are designed as independent systems. What if we equipped these appliances and their environments with the ability to communicate, sense and optimize their energy efficiency and use as a system?  See Agogino’s Distinctive Voices talk at the National Academy of Engineering addressing the potential to “green” the “Internet of Things”.

 

Smart Lighting in Etcheverry Hall

The overall goal of this project is to develop an intelligent daylighting system that can outperform today’s commercially available systems. Specifically, we aim to increase user satisfaction while minimizing energy consumption and expense. We propose to achieve this through the use of several intelligent techniques including influence-diagram based decision-making, fuzzy-based sensor validation and fusion, and agency. The assumed underlying sensor technology is Smart Dust Motes, a wireless sensing and communications platform currently under development at U.C. Berkeley and various private companies.

 

Jessica Granderson, Yao-Jung Wen and Alice Agogino are featured in the December issue of Energy Notes, from the California Energy Institute. UC Berkeley researchers are exploring new combinations of commercial lighting systems controlled by “smart” wireless sensor technology that can customize a room’s lighting to meet a range of pre-established conditions, including the occupant’s personal preference. Jessica Granderson tests the demonstration system with a remote control while Alice Agogino and Yao-Jung Wen watch. Read the full article: Wireless Smart Lighting System: Saves energy, responds to human preferences (pdf)

Presentations

Publications

1. Paulson, R., “Personalized Illuminance Modeling Using
   Inverse Modeling and Piecewise Linear Regression, MS Plan II Report, Spring 2012.
2. Moret, S., “Energy Harvesting and Data Analysis of Smart Lighting Project in Sutardja Dai Hall“, Dec. 2011, Draft Report.
3. Allen, M., “Human-centered control and energy monitoring feedback for occupants of open plan office spaces“, Honors Research Report, Dec. 2011.
4. Wen, Y.-J, and A.M. Agogino, “Control of a Wireless-Networked Lighting System in an Open-plan Office”, Journal of Lighting Research and Technology, Vol. 43 (2), June 2011, pp. 235-248.
5. Wen, Y.-J, and A.M. Agogino, “Personalized Dynamic Design of Networked Lighting for Energy-Efficiency in Open-Plan Offices”, Energy and Buildings, Vol. 43 (8), August 2011, pp. 1919-1924.
6. Wen, Y.-J, Wireless Sensor and Actuator Networks for Lighting Energy Efficiency and User Satisfaction, Ph.D. Dissertation, University of California, Berkeley, Fall 2008.
7. Wen, Y.-J., J. Bonnell and A.M. Agogino, “Energy Conservation Utilizing Wireless Dimmable Lighting Control in a Shared-Space Office,” in Proceedings of the 2008 Annual Conference of the Illuminating Engineering Society, Savannah, GA 2008.
8. Wen, Y.-J. and A.M. Agogino, “Wireless Networked Lighting Systems for Optimizing Energy Savings and User Satisfaction,” in Proceedings of 2008 IEEE Wireless Hive Networks Conference, Austin, TX, 2008.
9. Bonnell, J.T., Green Lighting: Wireless Lighting Systems Integration for Significant Energy Savings, Master’s Report, University of California, Berkeley, Spring 2008.
10. Agogino, A.M, J. Granderson and Y.-J. Wen, “Efficient Lighting by Sensing and Actuating with MEMS ‘Smart Dust Motes’: A Feasibility Study,” Final Report, Energy Innovations Small Grant (EISG) Program, Grant #03-20, March 2007.
11. Wen, Y.-J., J. Granderson, and A.M. Agogino, “Towards Embedded Wireless-Networked Intelligent Daylighting Systems for Commercial Buildings,” in Proceedings of IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Taichung, Taiwan, 2006, pp 326-331.
12. Wen, Y.-J., A.M. Agogino and K. Goebel, “Fuzzy Validation and Fusion for Wireless Sensor Networks“, in Proceedings of the ASME International Mechanical Engineering Congress, Anaheim, CA, 2004.
13. Wen, Y.-J, Smart Dust Sensor Mote Characterization, Validation, Fusion and Actuation, Master’s Report, University of California, Berkeley, Fall 2004.
14. Granderson J, Y.-J. Wen, A.M. Agogino, and K. Goebel, “Towards Demand-Responsive Intelligent Lighting With Wireless Sensing and Actuation“, in Proceedings of the IESNA (Illuminating Engineering Society of North America) 2004 Annual Conference, Tampa, FL, 2004, pp.265-274.
15. Sandhu J.S., A.M. Agogino and A.K. Agogino, “Wireless Sensor Networks for Commercial Lighting Control: Decision Making with Multi-agent Systems“, in Working Notes of the AAAI-04 Sensor Networks Workshop, San Jose, CA, 2004, pp. 88-92.
16. Dubberly M, Life-cycle Assessment of Intelligent Lighting System using Distributed Mote Network, University of California, Berkeley, Master’s Report, Fall 2003.
17. Yozell-Epstein R, Economic, Energy and User Needs Analysis of Intelligent Lighting Systems, Master’s Report, University of California, Berkeley, Spring 2003.
18. Agogino A.M., J. Granderson and S. Qiu, “Intelligent Sensor Validation and Fusion with Distributed ‘MEMS Dust’ Sensors,” [abstract only], in Proceedings of the AAAI 2002 Spring Symposium, 2002, pp. 51-57.

Teams

Director
Alice M. Agogino, Professor

 

Student Groups

Controls (meets Thursdays 1-2pm):

Graduate Students:

• Chandrayee Basu
• Ryan Paulson (head)

Undergraduate Students:

• Meghan Chandarana

BACnet/Mote Programming (meets Wednesdays 1-2pm, Fridays 2-3pm):

Graduate Students:

• Marlon Diaz, King Abdullah University of Science and Technology

Undergraduate Students:

• Louisa Avellar
• Alexander Chen
• Derek Tat (head)
• Charles Wang

 

Mote Power (meets Mondays 3-4pm):

Graduate Students:

• Stefano Moret (head)

Undergraduate Students:

• Olivia Yu

 

Packaging (meeting time to be scheduled):

Graduate Students:

• Josh Eggleston

Undergraduate Students:

• Michael Allen (head)
• Timothy Gassner

 

User Interface (meets Thursdays 12-1pm):

Undergraduate Students:

• Michael Allen (head)
• Anran Li
• Tommy Liu, Stanford University
• Allan Wang
• Tony Wen