Scientific Challenges


To tackle the technical challenges listed above, we plan to design a new-generation automatic chiller plant optimization. In Fig. 2, we present the architecture design of our new system. Generally speaking, the system consists of an online running component at chiller plant site, and an offline component running on cloud platform. The online component is directly connected to the chiller plant for data collection and control, together with an interface to building managers and onsite engineers. The offline component mainly runs on the cloud, with data management and learning modules running with external knowledge from domain experts or Internet.

Figure 2: The interaction with chiller plants, engineers, administrator and external information source.

The processing modules within the automatic optimization system follow the general workflow in Fig. 4. There are four core components of the optimization system, including the data collection component, the data analytics component, the online optimization component and performance evaluation component. The data collection component is responsible for sensor data preprocessing, especially on cleaning meaningless records. The data Analytics component builds models to understand the dynamics of a chiller plant, based on historical data collected and stored the cloud platform. The online optimization component runs algorithms to find optimal configuration of the chiller plant in real-time. The performance evaluation component estimates the optimality of the model and optimization algorithm, with the results fed back to the data analytical component for model improvement. The data collection and data analytics components utilize cloud computing technique, so that different chiller plants share the storage and computation resource on cloud for cheaper operation cost of the systems.


Figure 3: The general framework of the target optimization system for chiller plants.

We adopt the following technologies to address the challenges in the components, in order to improve the efficiency of chiller plants.


[TSL00] J. B. Tenenbaum, V. De Silva, J. C. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science 290(5500): 2319-2323, 2000.

[PY10] S. J. Pan, Q. Yang, A Survey on Transfer Learning, IEEE Transaction on Knowledge and Data Engineering, 22(10), 1345-1359, 2010.