TY - JOUR

T1 - A hybrid-model forecasting framework for reducing the building energy performance gap

AU - Chen, Xia

AU - Guo, Tong

AU - Kriegel, Martin

AU - Geyer, Philipp

N1 - Funding Information: We gratefully acknowledge the German Research Foundation (DFG) support for funding the project under grant GE 1652/3-2 in the Researcher Unit FOR 2363. We would like to thank Prof. Peng Xu and his research group at Tongji University, Shanghai, China, for data resources support.

PY - 2022/4

Y1 - 2022/4

N2 - The performance gap between predicted and actual energy consumption in the building domain remains an unsolved problem in practice. The gap exists differently in both current mainstream methods: the first-principles model and the machine learning (ML) model. Inspired by the concept of time-series decomposition to identify different uncertainties, we proposed a hybrid-model approach by combining both methods to minimize this gap: 1. Use the first-principles method as an encoding tool to convert the building static features and predictable patterns in time-series simulation results; 2. The ML method combines the results as extra inputs with historical records simultaneously, trains the model to capture the implicit performance difference, and aligns to calibrate the output. To extend this approach in practice, a new concept in the modeling process: Level-of-Information (LOI), is introduced to leverage the balance between the investment of simulation modeling detail and the accuracy boost. The approach is tested over a three-year period, with hourly measured energy load from an operating commercial building in Shanghai. The result presents a dominant accuracy enhancement: The hybrid-model shows higher accuracy in prediction with better interpretability; More important, it releases the practitioners from modeling workload and computational resources in refining simulation. In summary, the approach provides a nexus for integrating domain knowledge via building simulation with data-driven methods. This mindset applies to solving general engineering problems and leads to improved prediction accuracy.

AB - The performance gap between predicted and actual energy consumption in the building domain remains an unsolved problem in practice. The gap exists differently in both current mainstream methods: the first-principles model and the machine learning (ML) model. Inspired by the concept of time-series decomposition to identify different uncertainties, we proposed a hybrid-model approach by combining both methods to minimize this gap: 1. Use the first-principles method as an encoding tool to convert the building static features and predictable patterns in time-series simulation results; 2. The ML method combines the results as extra inputs with historical records simultaneously, trains the model to capture the implicit performance difference, and aligns to calibrate the output. To extend this approach in practice, a new concept in the modeling process: Level-of-Information (LOI), is introduced to leverage the balance between the investment of simulation modeling detail and the accuracy boost. The approach is tested over a three-year period, with hourly measured energy load from an operating commercial building in Shanghai. The result presents a dominant accuracy enhancement: The hybrid-model shows higher accuracy in prediction with better interpretability; More important, it releases the practitioners from modeling workload and computational resources in refining simulation. In summary, the approach provides a nexus for integrating domain knowledge via building simulation with data-driven methods. This mindset applies to solving general engineering problems and leads to improved prediction accuracy.

UR - http://www.scopus.com/inward/record.url?scp=85134042734&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2206.00460

DO - 10.48550/arXiv.2206.00460

M3 - Article

AN - SCOPUS:85134042734

VL - 52

JO - Advanced engineering informatics

JF - Advanced engineering informatics

SN - 1474-0346

M1 - 101627

ER -