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With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this paper, we present the “Solar+ Optimizer” (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.
Anand Krishnan Prakash; Kun Zhang; Pranav Gupta; David Blum; Marc Marshall; Gabe Fierro; Peter Alstone; James Zoellick; Richard Brown; Marco Pritoni. Solar Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids+. Energies 2020, 13, 3093 .
AMA StyleAnand Krishnan Prakash, Kun Zhang, Pranav Gupta, David Blum, Marc Marshall, Gabe Fierro, Peter Alstone, James Zoellick, Richard Brown, Marco Pritoni. Solar Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids+. Energies. 2020; 13 (12):3093.
Chicago/Turabian StyleAnand Krishnan Prakash; Kun Zhang; Pranav Gupta; David Blum; Marc Marshall; Gabe Fierro; Peter Alstone; James Zoellick; Richard Brown; Marco Pritoni. 2020. "Solar Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids+." Energies 13, no. 12: 3093.
Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open datasets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats. To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.
Gabe Fierro; Marco Pritoni; Moustafa Abdelbaky; Daniel Lengyel; John Leyden; Anand Krishnan Prakash; Pranav Gupta; Paul Raftery; Therese Peffer; Greg Thomson; David E. Culler; Berkeley Gabe Fierro Uc Berkeley; Berkeley Marco Pritoni Lawrence Berkeley National Laboratory; California John Leyden Uc Berkeley. Mortar. ACM Transactions on Sensor Networks 2020, 16, 1 -31.
AMA StyleGabe Fierro, Marco Pritoni, Moustafa Abdelbaky, Daniel Lengyel, John Leyden, Anand Krishnan Prakash, Pranav Gupta, Paul Raftery, Therese Peffer, Greg Thomson, David E. Culler, Berkeley Gabe Fierro Uc Berkeley, Berkeley Marco Pritoni Lawrence Berkeley National Laboratory, California John Leyden Uc Berkeley. Mortar. ACM Transactions on Sensor Networks. 2020; 16 (1):1-31.
Chicago/Turabian StyleGabe Fierro; Marco Pritoni; Moustafa Abdelbaky; Daniel Lengyel; John Leyden; Anand Krishnan Prakash; Pranav Gupta; Paul Raftery; Therese Peffer; Greg Thomson; David E. Culler; Berkeley Gabe Fierro Uc Berkeley; Berkeley Marco Pritoni Lawrence Berkeley National Laboratory; California John Leyden Uc Berkeley. 2020. "Mortar." ACM Transactions on Sensor Networks 16, no. 1: 1-31.
Mary Ann Piette; Reshma Singh; Anand Krishnan Prakash. Evaluation of the Need for and Design of a City Energy Operations System. Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization 2019, 97 -100.
AMA StyleMary Ann Piette, Reshma Singh, Anand Krishnan Prakash. Evaluation of the Need for and Design of a City Energy Operations System. Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization. 2019; ():97-100.
Chicago/Turabian StyleMary Ann Piette; Reshma Singh; Anand Krishnan Prakash. 2019. "Evaluation of the Need for and Design of a City Energy Operations System." Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization , no. : 97-100.
Fabricio Flores; Sirajum Munir; Matias Quintana; Anand Krishnan Prakash; Mario Bergés. Dataset. Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19 2019, 7 -9.
AMA StyleFabricio Flores, Sirajum Munir, Matias Quintana, Anand Krishnan Prakash, Mario Bergés. Dataset. Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19. 2019; ():7-9.
Chicago/Turabian StyleFabricio Flores; Sirajum Munir; Matias Quintana; Anand Krishnan Prakash; Mario Bergés. 2019. "Dataset." Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19 , no. : 7-9.
Dimitrios Stamoulis; Ting-Wu (Rudy) Chin; Anand Krishnan Prakash; Haocheng Fang; Sribhuvan Sajja; Mitchell Bognar; Diana Marculescu. Designing adaptive neural networks for energy-constrained image classification. Proceedings of the International Conference on Computer-Aided Design 2018, 1 .
AMA StyleDimitrios Stamoulis, Ting-Wu (Rudy) Chin, Anand Krishnan Prakash, Haocheng Fang, Sribhuvan Sajja, Mitchell Bognar, Diana Marculescu. Designing adaptive neural networks for energy-constrained image classification. Proceedings of the International Conference on Computer-Aided Design. 2018; ():1.
Chicago/Turabian StyleDimitrios Stamoulis; Ting-Wu (Rudy) Chin; Anand Krishnan Prakash; Haocheng Fang; Sribhuvan Sajja; Mitchell Bognar; Diana Marculescu. 2018. "Designing adaptive neural networks for energy-constrained image classification." Proceedings of the International Conference on Computer-Aided Design , no. : 1.
Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error).
Anand Krishnan Prakash; Susu Xu; Ram Rajagopal; Hae Young Noh. Robust Building Energy Load Forecasting Using Physically-Based Kernel Models. Energies 2018, 11, 862 .
AMA StyleAnand Krishnan Prakash, Susu Xu, Ram Rajagopal, Hae Young Noh. Robust Building Energy Load Forecasting Using Physically-Based Kernel Models. Energies. 2018; 11 (4):862.
Chicago/Turabian StyleAnand Krishnan Prakash; Susu Xu; Ram Rajagopal; Hae Young Noh. 2018. "Robust Building Energy Load Forecasting Using Physically-Based Kernel Models." Energies 11, no. 4: 862.
Sirajum Munir; Le Tran; Jonathan Francis; Charles Shelton; Ripudaman Singh Arora; Craig Hesling; Matias Quintana; Anand Krishnan Prakash; Anthony Rowe; Mario Berges. FORK. Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication 2017, 33 .
AMA StyleSirajum Munir, Le Tran, Jonathan Francis, Charles Shelton, Ripudaman Singh Arora, Craig Hesling, Matias Quintana, Anand Krishnan Prakash, Anthony Rowe, Mario Berges. FORK. Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication. 2017; ():33.
Chicago/Turabian StyleSirajum Munir; Le Tran; Jonathan Francis; Charles Shelton; Ripudaman Singh Arora; Craig Hesling; Matias Quintana; Anand Krishnan Prakash; Anthony Rowe; Mario Berges. 2017. "FORK." Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication , no. : 33.
Anand Krishnan Prakash; Vivek Chil Prakash; Bhavin Doshi; Uddhav Arote; Pallab Kumar Sahu; Krithi Ramamritham. Locating and Sizing Smart Meter Deployment in Buildings. Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems 2015, 1 .
AMA StyleAnand Krishnan Prakash, Vivek Chil Prakash, Bhavin Doshi, Uddhav Arote, Pallab Kumar Sahu, Krithi Ramamritham. Locating and Sizing Smart Meter Deployment in Buildings. Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems. 2015; ():1.
Chicago/Turabian StyleAnand Krishnan Prakash; Vivek Chil Prakash; Bhavin Doshi; Uddhav Arote; Pallab Kumar Sahu; Krithi Ramamritham. 2015. "Locating and Sizing Smart Meter Deployment in Buildings." Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems , no. : 1.
As buildings get smarter they need to be aware of their spaces and occupants to improve prediction and management of energy consumption and environment customization based on user preference. User identification is crucial to this. However, accuracy of identification, intrusiveness and cost are important factors that one considers before installing such a system. Accounting for these factors, we built a Smart-Door that incorporates fusion of not-so-smart sensors, soft information available and learning algorithms to build an economical and accurate user identification system that requires no user intervention to monitor the occupant count and identities in a shared office space that can be scaled up to a building. It provides real-time occupancy status for the area and it can also learn to identify new users. In addition to energy management, such a user identification system has significant applications including evacuation procedures and localizing malfunctioning appliances.
Vivek Chil Prakash; Anand Krishnan Prakash; Uddhav Arote; Vitobha Munigala; Krithi Ramamritham. Demo Abstract. Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems 2015, 1 .
AMA StyleVivek Chil Prakash, Anand Krishnan Prakash, Uddhav Arote, Vitobha Munigala, Krithi Ramamritham. Demo Abstract. Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems. 2015; ():1.
Chicago/Turabian StyleVivek Chil Prakash; Anand Krishnan Prakash; Uddhav Arote; Vitobha Munigala; Krithi Ramamritham. 2015. "Demo Abstract." Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems , no. : 1.
Understanding occupant-building interactions helps in personalized energy and comfort management. However, occupant identification using affordable infrastructure, remains unresolved. Our analysis of existing solutions revealed that for a building to have real-time view of occupancy state and use it intelligently, there needs to be a smart fusion of affordable, not-necessarily-smart, yet accurate enough sensors. Such a sensor fusion should aim for minimalistic user intervention while providing accurate building occupancy data. We describe an occupant detection system that accurately monitors the occupants’ count and identities in a shared office space, which can be scaled up for a building. Incorporating aspects from data analytics and sensor fusion with intuition, we have built a Smart-Door using inexpensive sensors to tackle this problem. It is a scalable, plug-and-play software architecture for flexibly realizing smart-doors using different sensors to monitor buildings with varied occupancy profiles. Further, we show various smart-energy applications of this occupancy information: detecting anomalous device behaviour and load forecasting of plug-level loads.
Nabeel Nasir; Kartik Palani; Amandeep Chugh; Vivek Chil Prakash; Uddhav Arote; Anand P. Krishnan; Krithi Ramamritham. Fusing Sensors for Occupancy Sensing in Smart Buildings. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 73 -92.
AMA StyleNabeel Nasir, Kartik Palani, Amandeep Chugh, Vivek Chil Prakash, Uddhav Arote, Anand P. Krishnan, Krithi Ramamritham. Fusing Sensors for Occupancy Sensing in Smart Buildings. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():73-92.
Chicago/Turabian StyleNabeel Nasir; Kartik Palani; Amandeep Chugh; Vivek Chil Prakash; Uddhav Arote; Anand P. Krishnan; Krithi Ramamritham. 2015. "Fusing Sensors for Occupancy Sensing in Smart Buildings." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 73-92.