Development and validation of a simplified online cooling load prediction strategy for a super high-rise building in Hong Kong

Abstract

Cooling load prediction is important and essential for many building energy efficient controls, such as morning start control of chiller plant. However, most of the existing methods are either too complicated or of unsatisfactory performance for online applications. A simplified online cooling load prediction method is therefore developed in this study. The method firstly selects a reference day for each day according to load profile similarity. The load profile of the reference day is taken as the initial prediction result of the cooling load. Secondly, the most correlated weather data is identified and its hourly predictions are used to calibrate the initial load prediction result based on the reference day. Lastly, the accuracy of the calibrated load prediction is enhanced using the prediction errors of the previous 2h. The developed load prediction method is validated in the case studies using the weather data purchased from the Hong Kong Observatory and the historical data from a super high-rise building in Hong Kong. The load prediction method is of low computation load and satisfactory accuracy and it can be used for online application of building load prediction.