文献来源：
A novel Transformer-based network forecasting method for building cooling loads
Long Li
a
,
*
, Xingyu Su
a
, Xianting Bi
b
, Yueliang Lu
c
, Xuetao Sun
d
a
School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China
b
Beijing Institute of Astronautics System Engineering, Beijing 100076, China
c
Aerospace Times FeiHong Technology Company Limited, Beijing 102199, China
d
Institute of Smart City, Shanghai University, Shanghai 200444, China
/transformer.pdf
文章摘要：
For cooling equipment management and scheduling optimization, accurate building cooling load forecasting technology is crucial. Currently, the physics-based forecasting models are too complex to achieve, and existing shallow-machine and deep learning algorithms are difffcult to capture and retain sequential information from historical building cooling loads, leading to insufffcient prediction accuracy. This paper considered the dependency relationship between time-series information in load data and proposed a building load prediction model based on a transformer network to improve the accuracy of building load prediction. This encoder-decoder block-based model can encode and decode all input data, capture sequence information from mapping vectors with user-deffned dimensions, and learn important features through the Attention mechanism. In addition, input features were analyzed to verify the importance of each input feature, and to explaine the reasons for the impact of used features on the TRN-based model. Finally, the performance of the proposed model is evaluated using real data from an offfce building. Compared with other existing methods, the proposed model has the best prediction accuracy (RMSE, MAE, R2 were 0.01, 0.03, and 0.98, respectively), and maintained the best predictive stability over a longer time (uncertainty ranged from − 11% to + 11%). The results show that the proposed method can support the development and optimal operation of energy-saving HVAC systems, thereby lowing power consumption.
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视频链接：
陈俊宇_基于transformer的建筑制冷负荷网络预测新方法_哔哩哔哩_bilibili