Work

Efficient room-level heat load prediction in buildings using spatiotemporal distribution characteristics

Year: 2025

Type: article

Abstract: Room-level heat load prediction is essential for optimizing building energy efficiency and managing intelligent HVAC systems. This study proposes a prediction method integrating spatiotemporal distribution features within a “global prediction + feature decomposition” framework to overcome traditional data-driven limitations. A spatial allocation matrix, based on building thermodynamics, quantifies heat load allocation across floors and orientations, while a temporal allocation matrix, derived from intermittent heating experiments, captures inter-room heat transfer effects . A thermodynamic model built with DesignBuilder and a ResGRU neural network enables overall heat load prediction, with spatiotemporal matrix decomposition ensuring rapid room-level estimations. Empirical results show that this method requires only total inlet heat metering for multi-scenario predictions, reducing sensor deployment while achieving 7.02 % accuracy. This approach supports zonal HVAC control and demonstrates strong potential for energy-efficient building applications.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2025.106169

Authors: Xin Tan, Kaixuan Xu, Yahui Wang, Qihui Yu, Yongheng Yu, Guoxin Sun

Institution: Inner Mongolia University of Science and Technology

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,620