Cooling load forecasting-based predictive optimisation for chiller plants

Cooling load forecasting-based predictive optimisation for chiller plants

TitleCooling load forecasting-based predictive optimisation for chiller plants
Publication TypeJournal Article
Year of Publication2019
AuthorsLan Wang, Eric WaiMing Lee, Richard KK Yuen, Wei Feng
JournalEnergy and Buildings
Volume198
Pagination261 - 274
Date PublishedJan-09-2019
ISSN03787788
Keywordsbuildings, China
Abstract

Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Instead of minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced.

DOI10.1016/j.enbuild.2019.06.016
Short TitleEnergy and Buildings