A novel method of creating machine learning-based time series meta-models for building energy analysis

Abstract

The meta-models have been widely used to replace computationally expensive engineering-based building energy models for model calibration, sensitivity analysis, and performance optimization of buildings. However, most meta-models are used for monthly or annual building energy analysis applying the same procedure as building energy or load prediction. A few studies thoroughly explore the characteristics of meta-models when creating hourly machine learning-based meta-models for building energy assessment, which is very different from conventional building load or energy prediction. Therefore, this paper proposes a new method of creating time series models for building energy analysis based on machine learning techniques. The main feature of this new method is to include two steps (model preselection and data folding) by taking advantage of the sequential nature during the process of constructing meta-models. A case study of office buildings is used to demonstrate the application of this new approach using the EnergyPlus program and R computational environment. The results indicate that the time-series meta-models constructed using this new method have very high accuracy with a moderate computational cost. For the best models, the R2 values are above 0.99 and the CV(RMSE) (coefficient variation of root mean square error) values are below 0.06 for both heating and cooling energy prediction. The ensemble machine learning models, including Cubist, gradient boosting machine, and stacking, are recommended to create the meta-models for hourly building energy analysis. The distributed lag models combined with these ensemble models can be used to utilize the time-series features of hourly building energy assessment. The method proposed here can be extended to the time-series energy analysis in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind).