A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study

Buildings

Wenqi Jia¹  Yuhao Zhou²  Yurong Xie²  Yumin Liang³  Xiaolei Yuan³  Yiqun Pan³ 

[1] Department of Mechanical Engineering, Texas A&M University, College Station, TX 77840, USA;Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China;School of Mechanical Engineering, Tongji University, Shanghai 201804, China;

关键词: building load forecasting;    meta-modeling;    deep learning;    CNN;    Seq2Seq;    district;   

DOI  :  10.3390/buildings12020177

来源: DOAJ

【 摘 要 】

This paper proposes a meta-modeling workflow to forecast the cooling and heating loads of buildings at individual and district levels in the early design stage. Seven input variables, with large impacts on building loads, are selected for designing meta-models to establish the MySQL database. The load profiles of office, commercial, and hotel models are simulated with EnergyPlus in batches. A sequence-to-sequence (Seq2Seq) model based on the deep-learning method of a one-dimensional convolutional neural network (1D-CNN) is introduced to achieve rapid forecasting of all-year hourly building loads. The method performs well with the load effective hour rate (LEHR) of around 90% and MAPE less than 10%. Finally, this meta-modeling workflow is applied to a district as a case study in Shanghai, China. The forecasting results well match the actual loads with R² of 0.9978 and 0.9975, respectively, for the heating and cooling load. The LEHR value of all-year hourly forecasting loads is 98.4%, as well as an MAPE of 4.4%. This meta-modeling workflow expands the applicability of building-physics-based methods and improves the time resolution of conventional data-driven methods. It shows small forecasting errors and fast computing speed while meeting the required precision and convenience of engineering in the building early design stage.

【 授权许可】

Unknown