In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing or new buildings. Transfer learning aims to enhance the performance of a target learner exploiting knowledge from related and similar environments. This study conducted a suite of experiments that leveraged 250 data-driven models based on a synthetic dataset of a building archetype to study the influence of data availability, energy efficiency level, occupancy and climate for the transfer process of thermal dynamics. The performance of the transfer learning process was compared against a classical machine learning approach. The results suggest that building thermal dynamics can be effectively transferred under the same climatic conditions, increasing performance when dealing with different occupancy schedules, efficiency levels and low data availability. Furthermore, the paper compares the performance of both transfer learning and machine learning approaches in an online fashion, to support the implementation in real-world deployment.
Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics / Pinto, Giuseppe; Messina, Riccardo; Li, Han; Hong, Tianzhen; Piscitelli, Marco Savino; Capozzoli, Alfonso. - In: ENERGY AND BUILDINGS. - ISSN 0378-7788. - ELETTRONICO. - 276:(2022), p. 112530. [10.1016/j.enbuild.2022.112530]
Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics
Pinto, Giuseppe;Messina, Riccardo;Piscitelli, Marco Savino;Capozzoli, Alfonso
2022
Abstract
In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing or new buildings. Transfer learning aims to enhance the performance of a target learner exploiting knowledge from related and similar environments. This study conducted a suite of experiments that leveraged 250 data-driven models based on a synthetic dataset of a building archetype to study the influence of data availability, energy efficiency level, occupancy and climate for the transfer process of thermal dynamics. The performance of the transfer learning process was compared against a classical machine learning approach. The results suggest that building thermal dynamics can be effectively transferred under the same climatic conditions, increasing performance when dealing with different occupancy schedules, efficiency levels and low data availability. Furthermore, the paper compares the performance of both transfer learning and machine learning approaches in an online fashion, to support the implementation in real-world deployment.File | Dimensione | Formato | |
---|---|---|---|
Sharing is caring.pdf
non disponibili
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
3.88 MB
Formato
Adobe PDF
|
3.88 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Postprint author - Sharing is caring.pdf
Open Access dal 02/10/2024
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Creative commons
Dimensione
7.56 MB
Formato
Adobe PDF
|
7.56 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2974664