Improving Building Temperature Forecasting: A Data-driven Approach with
  System Scenario Clustering
Dafang Zhao Zheng Chen Zhengmao Li Xiaolei Yuan Ittetsu Taniguchi
Heat, Ventilation and Air Conditioning (HVAC) systems play a critical role in
maintaining a comfortable thermal environment and cost approximately 40% of
primary energy usage in the building sector. For smart energy management in
buildings, usage patterns and their resulting profiles allow the improvement of
control systems with prediction capabilities. However, for large-scale HVAC
system management, it is difficult to construct a detailed model for each
subsystem. In this paper, a new data-driven room temperature prediction model
is proposed based on the k-means clustering method. The proposed data-driven
temperature prediction approach extracts the system operation feature through
historical data analysis and further simplifies the system-level model to
improve generalization and computational efficiency. We evaluate the proposed
approach in the real world. The results demonstrated that our approach can
significantly reduce modeling time without reducing prediction accuracy.