Work

Similarity-Driven Transfer Learning for Short-Term Building Energy Consumption Forecasting

Year: 2026

Type: article

Abstract: When considering the use of renewable energy in buildings, accurate short-term energy consumption prediction is critical for achieving a balance between building energy supply and demand. While multi-source transfer learning has improved prediction accuracy, existing studies predominantly focus on total consumption, overlooking the significance of sub-metered energy characteristics. Furthermore, the effectiveness of similarity metrics in such predictions remains underexplored. Therefore, this study introduced a novel multi-source transfer learning model that integrates similarity measurement and sub-metering. This research offers practical insights for optimizing energy consumption predictions in commercial buildings, supports refined energy management strategies and contributes to the development of sustainable, low-carbon buildings.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/e3sconf/202668910003/pdf

Authors: Fan Yang, Xiaofeng Li

Institution: Tsinghua University

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Sustainable cities and communities

Open Access status: green

OpenAlex ID: W7125411865

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