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
"An overview of data tools for representing and managing building information and performance data." Renewable and Sustainable Energy Reviews 147 (2021) 111224. .
"Fault “Auto-correction” for HVAC Systems: A Preliminary Study." The 6th International High Performance Buildings Conference at Purdue. 2021. .
"COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility." BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationProceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Virtual Event JapanNew York, NY, USA: ACM, 2020. .
"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. .
"Of impacts, agents, and functions: An interdisciplinary meta-review of smart home energy management systems research." Energy Research & Social Science 68 (2020) 101555. .
"Advanced control sequences and FDD technology. Just shiny objects, or ready for scale?." ACEEE Summer Study 2020. 2020. .
"Beyond Curtailment and Efficiency: Identifying Household Energy- and Water-Saving Measure Classes." ACEEE Summer Study. 2020. .
"Can We Fix It Automatically? Development of Fault Auto-Correction Algorithms for HVAC and Lighting Systems." ACEEE Summer Study. 2020. .
"Developing and Evaluating Metrics for Demand Flexibility in Buildings: Comparing Simulations and Field Data." ACEEE Summer Study. 2020. .
"Harvesting the low-hanging fruit of high energy savings -- Virtual Occupancy using Wi-Fi Data." ACEEE Summer Study. Virtual, 2020. .
"Re-Envisioning RCx: Achieving Max Potential HVAC Controls Retrofits through Modernized BAS Hardware and Software." 2020 Summer Study on Energy Efficiency in Buildings. 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. .
"Semantic Interoperability to Enable Smart, Grid-Interactive Efficient Buildings." 2020 Summer Study on Energy Efficiency in Buildings. 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. .