Time-series Forecast for Indoor Zone Air Temperature with Long Horizons: A Case Study with Sensor-based Data from a Smart Building

TL;DR

The paper tackles the need for fast, long-horizon forecasts of indoor zone temperature to enable energy-efficient HVAC control under climate variability. It proposes a hybrid data-driven and thermodynamic approach, delivering a 2-week-ahead forecast using an ensemble built on XGBoost with self-regression and hierarchical time-series structure, validated on a large sensor-based dataset from a smart building. Results show MAE of 4.20 and RMSE of 4.8 (°F), with notable differences across VAV boxes, underscoring spatial dependencies and the potential for location-aware corrections. The work points to practical downstream use in data-driven model predictive control and energy-flexibility optimization, scalable to cloud deployments for commercial buildings.

Abstract

With the press of global climate change, extreme weather and sudden weather changes are becoming increasingly common. To maintain a comfortable indoor environment and minimize the contribution of the building to climate change as much as possible, higher requirements are placed on the operation and control of HVAC systems, e.g., more energy-efficient and flexible to response to the rapid change of weather. This places demands on the rapid modeling and prediction of zone air temperatures of buildings. Compared to the traditional simulation-based approach such as EnergyPlus and DOE2, a hybrid approach combined physics and data-driven is more suitable. Recently, the availability of high-quality datasets and algorithmic breakthroughs have driven a considerable amount of work in this field. However, in the niche of short- and long-term predictions, there are still some gaps in existing research. This paper aims to develop a time series forecast model to predict the zone air temperature in a building located in America on a 2-week horizon. The findings could be further improved to support intelligent control and operation of HVAC systems (i.e. demand flexibility) and could also be used as hybrid building energy modeling.

Figures (8)

• Figure 1: Floorplan of the case building
• Figure 2: Input and Output Used for the Model
• Figure 3: Data-driven thermodynamics model
• Figure 4: Post-launch performance of indoor air temperature changes for the 1st VAV
• Figure 5: Post-launch performance of indoor air temperature changes for the 2nd VAV