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Smart People, Products & Buildings on the Smart GridMaxTech & Beyond: Design Competition for Ultra-Low-Energy Use Appliances & EquipmentUser-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 ExplorationObjectives: 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 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 is critical for space exploration and environmental/machine monitoring. We propose to build on NASA’s experience with a domestic test bed in Sustainability Base, along with a simplified mockup of the skin of a space vehicle. 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. |
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Sensor Data from 4th floor of CITRIS: http://best.berkeley.edu/~smartlighting/node_list.html
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
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”.
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.
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Check the photo gallery for pictures of the system implementation.
This research has been sponsored by:
 GE Global Research |
 UC Energy Institute |
 PIER Program |
 VTT Finland |
 NASA Ames |
