Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism

This study focuses on studying the impact of temporal resolution of energy data from a commercial building on data exploration and learning applications. The goal is to improve the consistency of the modelling techniques and define the best-unified resolution of data for different applications such as data exploration, consistency, load profile extraction, and forecasting for commercial buildings. A three-step process is proposed to evaluate the data resolution effects on mining and learning applications. The first step, the data exploration and consistency, transforms the raw data into a human interpretable form using different temporal features to understand the dependency of the load consumption of the building. In the second step, the K-means clustering technique is used to extract the typical load profiles for all data granularities to deduce information related to the operational behaviour of the building throughout the year. Finally, the long short-term memory model is evaluated for building load forecasting in a univariate and multivariate approach in the last step. The results demonstrate that higher-resolution data does not necessarily assure clear relationships between the operational parameters of a commercial building using data exploration techniques. Furthermore, increasing the granularity of the data did not affect the extracted number of clusters or the load profiles overall shape (peak points). In contrast, to load profile extraction, the obtained results are improved in building load forecasting with NRMSE from 0.125 to 0.028 for daily to 1 min resolution data. Overall, considering the balance of accuracy and processing time the 15-minute resolution data with a univariate approach can perform the best.