PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Karim Boubouh, Robert Basmadjian (), Omid Ardakanian, Alexandre Maurer and Rachid Guerraoui
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Karim Boubouh: School of Computer Science, UM6P, Hay Moulay Rachid, Ben Guerir 43150, Morocco
Robert Basmadjian: Department of Informatics, Clausthal University of Technology, Julius-Albert-Str. 4, 38678 Clausthal-Zellerfeld, Germany
Omid Ardakanian: Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
Alexandre Maurer: School of Computer Science, UM6P, Hay Moulay Rachid, Ben Guerir 43150, Morocco
Rachid Guerraoui: Distributed Computing Laboratory, EPFL, Bat INR 311 Station 14, 1015 Lausanne, Switzerland

Energies, 2023, vol. 16, issue 18, 1-22

Abstract: Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for on-device training, enabling customized thermal experiences in homes. However, leveraging the flexibility offered by on-device learning has been hindered by the absence of a tailored learning scheme that allows for accurate on-device training of thermal models. Traditional centralized learning (CL) and federated learning (FL) schemes rely on a central server that controls the learning experience, compromising the home’s privacy and requiring significant energy to operate. To address these challenges, we propose PePTM , a personalized peer-to-peer thermal modeling algorithm that generates tailored thermal models for each home, offering a controlled learning experience with a minimal training energy footprint while preserving the home’s privacy, an aspect difficult to achieve in both CL and FL. PePTM consists of local and collaborative learning phases that enable each home to train its thermal model and collaboratively improve it with a set of similar homes in a peer-to-peer fashion. To showcase the effectiveness of PePTM , we use a year’s worth of data from US homes to train thermal models using the RNN time-series model and compare the data across three learning schemes: CL, FL, and PePTM , in terms of model performance and the training energy footprint. Our experimental results show that PePTM is significantly energy-efficient, requiring 695 and 40 times less training energy than CL and FL, respectively, while maintaining comparable performance. We believe that PePTM sets the stage for new avenues for on-device thermal model training, providing a personalized thermal experience with reduced energy consumption and enhanced privacy.

Keywords: smart thermostats; peer-to-peer machine learning; personalized models; energy-efficiency; thermal models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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