Feature selection for indoor temperature prediction in large-space buildings based on transfer entropy and life cycle cost

Abstract

Indoor temperature prediction as an essential reference for heating, ventilation, and air-conditioning (HVAC) system tuning has boomed in recent years, with the development of information technology. But for data-driven techniques, inputs as key factors in prediction accuracy, learning efficiency and generalization capacity, did not pay enough attention. Existing research focused only on prediction accuracy by copying previous practices in other fields like medicine and hydrology, without considering prediction horizons and economic costs, which is meaningless and even may mislead the configuration of the monitoring system, especially for large-space buildings with long-span space, diverse zones and coupling parameters. In this work, a novel hybrid feature selection technique for indoor temperature prediction was developed. Herein, transfer entropy (TE) was introduced into filter to evaluate importance of features, where Max-min TE as a gauge was defined to cope with time delays in HVAC system; Then gated recurrent network was taken in wrapper as a bridge to transform evaluations in filter into contributions to prediction; Lastly, life cycle cost was employed as a referee to judge cost-efficient alternatives. The proposed technique was validated by time-series data collected from a library and system energy consumption baseline simulated with TRNSYS to suggest optimal feature subsets under different prediction horizons.