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Smart People, Products and Buildings on the Smart Grid

The United States and the planet face the grand challenge of providing affordable energy that minimizes negative consequences of local and global environmental change. Tremendous challenges remain along the energy systems pathway to sustainability: scientific and technological research, technology design and system integration; innovative policies; public acceptance and use; and both supply-side and end-use management.   This initiative promises to dramatically expand the capacity of the emerging smart grid to create innovations in the integration of renewable energy systems and building energy efficiency, and to create a paradigm shift to smarter interactions between people and the way they interact with products and services in buildings.  The approach is to integrate smart technologies at a variety of scales, focusing first on the human-centered design for building inhabitants and their workspaces, and then moving through the building, neighborhood, and grid. The ultimate goal is to enable the co-evolution and integration of renewable and building energy systems to reduce overall energy consumption while successfully addressing people-based design and performance criteria.

This initiative focuses on research in system sensing and modeling, systems integration, life-cycle supply chain analysis, performance monitoring, product design and decision support for demand-side energy assessment, reduction, and management at the scale of buildings and communities. In conjunction with building science and life-cycle environmental and supply chain modeling, a human-centered design approach will be used to develop and design an infrastructure for integrated renewable energy generation, energy storage and smart products (e.g., smart lighting, thermal comfort, and refrigerator systems) for low-energy buildings.  Possible deliverables include models and a decision-support tool for life-cycle assessment and performance monitoring of smart products and energy services in buildings on the smart grid. Another research direction would be the development of frameworks to optimize and manage distributed renewable energy systems with “smart” loads, including thermostatically-controlled appliances and plug-in electric vehicle storage. Finally, multi-systems science research will be used to make recommendations for sustainable energy guidelines, policies, and standards. The metrics used for evaluation will recognize the concerns of each scale, and include health, satisfaction, comfort and performance of building occupants, as well as reductions in energy use and environmental emissions.

Every electronic appliance 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? AI techniques are particularly suited for dealing with the challenges of enabling these Smart Products in the Smart Grid with the ability to handle sensor uncertainty, sensor fusion, inference, diagnostics and distributed decision-making.

Our Smart Lighting project – the application of wireless sensor networks to customizable commercial lighting control. This example requires decision making in the face of uncertainty, with needs for system self-configuration and learning. As two-thirds of electricity generated in the US is for commercial buildings, and lighting consumes 40% of this, the application also has the potential for significant energy savings. The goal is to leverage wireless sensor networks (WSN) to create an intelligent, economical solution for reducing energy costs – and overall societal energy usage – while improving individual lighting comfort levels. The talk will present the results of two years of testing on an installed Smart Lighting system, along with plans for implementation of the technology to NASA’s Sustainable Base – a new green building at NASA Ames. Future research includes developing multi-agent techniques involving distributed learning over a wider range of internet appliances. See some of our related work on the Internet of Things and Next Digital.

Sponsors and Award Reports:

Awards:

  • Leon Gaster Best Paper Award for Lighting Technology for 2011 for the paper “Control of Wireless-networked Lighting in Open-plan Offices” (with Yao-Jung Wen) published in Volume 43 Issue 2 of Lighting Research & Technology. The award was announced at the Society of Light and Lighting’s Annual General Meeting and Awards evening on 29 May 2012 at ZSL, Regent’s Park, London.
  • Finalist, Smart Lighting Project, Venture Lab Competition, Center for Entrepreneurship and Technology, 2007.

Publications: 

  1. “Life- Cycle Assessment of an Intelligent Lighting System Using a Distributed Wireless Sensor ‘Mote’ Network”, (with M.A. Dubberly and A. Horvath), Proceedings of the 2004 IEEE International Symposium on Electronics and the Environment and the IAER Electronics Recycling Summit: Life-Cycle Environmental Stewardship for Electronic Products, (May 10-13, 2004, Phoenix, Scottsdale, AZ, http://www.iseesummit.org/).Citations
  2. “Wireless Sensor Networks for Commercial Lighting Control: Decision Making with Multi-agent Systems”, (with J. Sandhu and A.K. Agogino), Proceedings of the AAAI-04 Workshop on Sensor Networks. pp. 88-92, 2004. Citations
  3. “Towards Demand-Responsive Intelligent Daylighting with Sensing and Actuation.” (with J. Granderson, Y. Wen and K. Goebel), Proceedings of the 2004 IESNA (Illuminating Engineering Society of North America) Annual Conference, (Tampa, FL, July 25-28, 2004), IESNA, pp. 265-274. Citations
  4. “Towards Embedded Wireless-Networked Intelligent Daylighting Systems for Commercial Buildings,”, (with Y.-J. Wen and J. Granderson) Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, IEEE Computer Society, #0-7695-2553-9/06, 2006. Citations.
  5. “Intelligent Office Lighting: Demand-Responsive Conditioning and Increased User Satisfaction”, (with J. Granderson). LEUKOS Journal, IESNA (Illuminating Engineering Society of North America) vol. 2 (3), Jan. 2006. Pre-publication version (pdf). Citations.
  6. “Wireless Networked Lighting Systems for Optimizing Energy Savings and User Satisfaction,” (with Y.-J. Wen), Proceedings of Wireless Hive Networks Conference, IEEE 978-1-4244-2849-6/08, 2008. Citations.
  7. “Energy Conservation Utilizing Wireless Dimmable Lighting Control in a Shared-Space Office”, (with Y-J Wen, J. Bonnell), Proceedings of the 2008 Annual Conference of the Illuminating Engineering Society, Savannah, GA 2008. PresentationSlides. Citations.
  8. “Control of a Wireless-Networked Lighting System in an Open-plan Office”, (with Y.-J. Wen), Journal of Lighting Research and Technology, Vol. 43 (2), June 2011, pp. 235-248. Citations. Won the Leon Gaster Best Paper Award for Lighting Technology, 2011.
  9. “Personalized Dynamic Design of Networked Lighting for Energy-Efficiency in Open-Plan Offices” (with Y.-J. Wen), Energy and Buildings, Vol. 43 (8), August 2011, pp. 1919-1924. Citations.
  10. “Inverse Modeling Using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting”, (with R. Paulson, C. Basu and S. Poll), in M. van Sinderen, O. Postolache, and C. Benavente-Peces (Eds.), SENSORNETS 2013: Proceedings of the 2nd International Conference on Sensor Networks, Barcelona, Spain, 19-21 February 2013 (pp. 213-221). Portugal: SCITEPRESS.
  11. “Sensor-Based Predictive Modeling for Smart Lighting in Grid-Integrated Buildings”, (with C. Basu, J. Caubel, K. Kim, E. Cheng, A. Dhinakaran, R. Martin), IEEE Sensors , special issue on Sensing Technologies for Intelligent Urban Infrastructures, IEEE, Vol. 14 (12), December 2014, pp. 4216-4229. 10.1109/JSEN.2014.2352331. Citations
  12. “Affordable and Personalized Lighting Using Inverse Modeling and Virtual Sensors”, (with C. Basu, B. Chu, J. Richards, A. Dhinakaran, and R. Martin), Proceedings of the SPIE Smart Structures Technologies for Civil, Mechanical and Aerospace System, Vol. 9061, 8 March 2014. doi:10.1117/12.2048681

Presentations:

News:

Wireless Smart Lighting: Saves, Responds to Human Preferences, Energy Notes, Vol. 4, (2), Sep 2006,

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User-Centric Model-Predictive Lighting Retrofit System

Research Partner: NASA Ames Sustainability Base

Background

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.

Potential Energy and/or Cost Savings

Our system will offer savings of more than 1.32 quads of commercial primary energy in the United States. Of the total average estimated energy savings, approximately 20% is from lighting and 10% from cooling with some extended energy savings, assuming that better control over heat gain will facilitate adoption of more energy-efficient cooling technologies in moderate climates in the country. A streamlined and less resource intensive commissioning and retrofitting process coupled with accurate model-based predictions of expected energy and cost savings could enhance the implementation of lighting retrofits in 21% of the target commercial buildings (assuming an increase in the commercial building stock by the time of product co

mmercialization and licensing). A further 15%-49% of indirect cooling energy savings is possible, depending on the climate zone, due to better adoption of thermally active systems. Cost savings are from a reduction of 50% or more in person-hours required for installation and commissioning.

Future Commercialization

The expected energy and cost savings should win recognition for our system in the current intelligent lighting market as a stand-alone solution or as an add-on to existing best-in-the-market intelligent lighting systems. This integrated solution to intelligent and energy-efficient lighting retrofit systems will work well with the most promising, emerging smart grid market technologies, as well.