Marco Pritoni is a Research Scientist at Lawrence Berkeley National Laboratory where he conducts research in the area of advanced controls, data-driven modeling and data analytics applied to buildings. He leads a team of researchers working on optimization of building operation and building-to-grid interaction. Marco has a multidisciplinary background spanning mechanical engineering, building science, data science and human behavior. He holds a PhD and MS in Mechanical and Aeronautical Engineering from UC Davis and MS in Industrial Engineering from the University of Bologna, Italy.
Mechanical and Aerospace Engineering, Ph.D, University of California, Davis, 2016
Mechanical and Aerospace Engineering, M.Sc, University of California, Davis, 2013
Industrial and Management Engineering, M.Sc. (Laurea Magistrale), University of Bologna (Italy), 2003
"Interactive Metadata Integration with Brick." BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Japan, 2020. .
"Shepherding Metadata Through the Building Lifecycle." BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Japan, 2020. .
"Deep Reinforcement Learning in Buildings: Implicit Assumptions and their Impact." RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities. 2020. .
"Advanced control sequences and FDD technology. Just shiny objects, or ready for scale?." ACEEE Summer Study 2020. 2020. .
"Harvesting the low-hanging fruit of high energy savings -- Virtual Occupancy using Wi-Fi Data." ACEEE Summer Study. Virtual, 2020. .
"Resilient buildings for fire-adapted landscapes: EE and flexible loads integrated with solar and storage microgrids." ACEEE Summer Study 2020. 2020. .
"Who Controls Energy in the Smart Home? A Multidisciplinary Taxonomy." 2020 Summer Study on Energy Efficiency in Buildings. 2020. .
"Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids." Energies 13.12 (2020) 3093. .
"Mortar: An Open Testbed for Portable Building Analytics." ACM Transactions on Sensor Networks 16.1 (2020) 1 - 31. .
"Machine Learning for Automated Extraction of Building Geometry." The American Council for an Energy-Efficient Economy. 2020. .
"Comparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs." IEEE PowerTech Milan 2019. 2019. .
"Using data from connected thermostats to track large power outages in the United States." Applied Energy 256 (2019) 113940. .
"Inferring occupant counts from Wi-Fi data in buildings through machine learning." Building and Environment 158 (2019) 281 - 294. .
Energy Reporting: Device Demonstration, Communication Protocols, and Codes and Standards. Sacramento: California Energy Commission, 2019. .
"What can connected thermostats tell us about American heating and cooling habits?." ECEEE 2019 SUMMER STUDY 2019. .
"Towards a Scalable Model for Smart Buildings." ACEEE Summer Study on Energy Efficiency in Buildings 2018. .
"Home energy management (HEM) database: A list with coded attributes of 308 devices commercially available in the US." Data in Brief 16 (2018) 71 - 74. .
"Dealing with Expected Thermal Discomfort." ACEEE Summer Study on Energy Efficiency in Buildings 2018. .
"Smart Home Energy Management: Use Cases and Savings Opportunities." ACEEE Summer Study on Energy Efficiency in Buildings 2018. .
"Categories and functionality of smart home technology for energy management." Building and Environment 123 (2017) 543 - 554. .
"Occupant thermal feedback for improved efficiency in university buildings." Energy and Buildings 144 (2017) 241 - 250. .
Accessing Wi-Fi Data for Occupancy Sensing. 2017. LBNL-2001053. .
"Operating Systems for Small/Medium Commercial Buildings." Intelligent Building Control Systems. . Cham: Springer International Publishing, 2017. 45 - 69. .
"Energy efficiency and the misuse of programmable thermostats: The effectiveness of crowdsourcing for understanding household behavior." Energy Research & Social Science 8 (2015). .