YOUNG SCIENTIST LECTURE
Speaker: MR. Giuseppe Pinto
Title: Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
Speaker bio: Mr. Giuseppe Pinto is a PhD student in Energetics at BAEDA Lab, in Politecnico di Torino, currently a visiting researcher at Lawrence Berkeley National Laboratory, California, USA. His current research focuses on the application of machine learning techniques to enhance energy flexibility, using surrogate models to speed-up building internal dynamics simulations and data-driven controllers in a multi-agent setting to improve energy management in grid-interactive buildings. Mr. Pinto received his MS in Energy Engineering at Politecnico di Torino in 2019, after completing his MS thesis at Aalto University, Finland. He has a multidisciplinary background in mechanical and energy engineering, operational research and machine learning.
Supporting institution: Department of Energy, TEBE Research Group, BAEDA lab, Politecnico di Torino Lawrence Berkeley National Laboratory, California, USA
ADAPEN paper link: https://doi.org/10.1016/j.adapen.2022.100084
Abstract: Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building’s ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.
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