Development of a clustering-based morning start time estimation algorithm for space heating and cooling

Abstract

The morning start time of heating and cooling equipment plays an important role in the energy and comfort performance of buildings. Existing algorithms to guide this decision require either many data types with a consistent labelling nomenclature or a detailed calibrated model. In this paper, a model-free clustering-based morning start time estimation algorithm is put forward. The algorithm inputs only four types of data: indoor and outdoor temperatures, and heating and cooling energy use, and does not require any information regarding the location of the temperature sensors. The algorithm consists of four steps. The first one employs clustering to form groups of zones with a similar temperature response. The second one searches for inflection points to identify cluster temperature change rates during morning start-up periods. The third one determines the start time based on previous morning start-up temperature change rates. The last one estimates the energy savings potential by using bivariate change point models. The algorithm was developed by using a dataset from a large office building. Through hierarchical clustering, the data from 142 temperature sensors were consolidated to only seven clusters. The median morning start-up temperature change rates in individual clusters were between 0.3°C/h and 0.8°C/h for heating, and between -0.5°C/h and -1.2°C/h for cooling. The savings potential by tuning daily start times based on this information was estimated as 3% and 7% for heating and cooling, respectively