  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. The result and
source data are available at
https://github.com/ResearchGroup-G/PerformanceGap-Hybrid-Approach.
