Few-sample model training assistant: A meta-learning technique for building heating load forecasting based on simulation data

Yakai Lu, Xingyu Peng, Conghui Li, Zhe Tian, Xiangfei Kong and Jide Niu

Energy, 2025, vol. 317, issue C

Abstract: Accurately predicting building heating loads with limited data is a challenge. Utilizing data from other buildings to create pre-trained models can assist in model training for the target building, such as transfer learning. However, obtaining high-quality source data for different target buildings is difficult in practice, impacting the implementation of this method. This paper introduces a Few-Sample Model Training Assistant (FSMTA) for predicting building heating loads. Firstly, prototype building simulation models of target building type are constructed, followed by Monte Carlo method for a large amount of simulation data of buildings with different parameters. Subsequently, a meta-model is developed by MAML meta-learning method to extract common features of these buildings through the simulation data, which is used as the FSMTA. The FSMTA was validated in high-rise residential buildings. 600 simulated buildings were used as source data to obtain the FSMTA. Another 40 simulated buildings and 1 actual building were used for testing under 7 different few-sample conditions. The results show that the FSMTA can reduce the average error by 4.11 %–36.75 % compared to direct learning and by 4.98 %–10.28 % compared to the multi-source transfer learning method.

Keywords: Data-driven load prediction; Few-sample prediction; Simulation model; Meta learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225001513
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:317:y:2025:i:c:s0360544225001513

DOI: 10.1016/j.energy.2025.134509

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().