Development and Comparison of Deep Learning methods for the Heating and Cooling loads Prediction of Buildings

DOI

10.2991/978-94-6463-726-7_46How to use a DOI?

Keywords

factorization-machine; deep neural network; load prediction; cooling load; building attributes

Abstract

The building industry’s high energy consumption and greenhouse gas emissions make accurate heating and cooling load prediction crucial. Traditional white box and black box methods have limitations. This paper explores Deep Neural Networks (DNNs) and the Deep - FM model, which combines Factorization Machines (FM) and DNNs. The DNN imitates the human brain, while Deep - FM explicitly captures high - order feature interactions and learns non - linear patterns. Using a dataset from the UCI machine learning repository with 768 data points and 10 variables related to building attributes, the performance of these two models was evaluated. Four metrics (MAE, MSE, MAPE, and R2) were used. The model structures were optimized, and the results show that Deep - FM is more effective for simpler models and limited data, while DNN excels in complex scenarios with sufficient data. The performance of both models improves with more training data. Deep - FM outperforms DNN in predicting heating and cooling loads under small training datasets. Additionally, deep learning algorithms show much higher accuracy than shallow learning algorithms in predicting cooling load.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiangyan Deng
AU  - Xinzhao Dong
AU  - Xu Ding
AU  - Zheng Yao
AU  - Da Li
AU  - Tonghui Li
PY  - 2025
DA  - 2025/06/13
TI  - Development and Comparison of Deep Learning methods for the Heating and Cooling loads Prediction of Buildings
BT  - Proceedings of the 2024 6th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2024)
PB  - Atlantis Press
SP  - 473
EP  - 481
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-726-7_46
DO  - 10.2991/978-94-6463-726-7_46
ID  - Deng2025
ER  -