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

TL;DR

This paper develops a hybrid physics and data-driven time series model to predict indoor zone air temperatures over a two-week horizon, aiding HVAC control and energy modeling in smart buildings.

Contribution

It introduces a novel hybrid forecasting approach specifically designed for long-horizon indoor temperature prediction in smart building environments.

Findings

Effective two-week temperature forecasts achieved

Hybrid model outperforms traditional methods

Potential to enhance HVAC control and energy efficiency

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.