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In this paper, we are interested in building a domain knowledge based deeplearning framework to solve the chiller plants energy optimization problems.Compared to the hotspot applications of deep learning (e.g. imageclassification and NLP), it is difficult to collect enormous data for deepnetwork training in real-world physical systems. Most existing methods reducethe complex systems into linear model to facilitate the training on smallsamples. To tackle the small sample size problem, this paper considers domainknowledge in the structure and loss design of deep network to build a nonlinearmodel with lower redundancy function space. Specifically, the energyconsumption estimation of most chillers can be physically viewed as aninput-output monotonic problem. Thus, we can design a Neural Network withmonotonic constraints to mimic the physical behavior of the system. We verifythe proposed method in a cooling system of a data center, experimental resultsshow the superiority of our framework in energy optimization compared to theexisting ones.

Monotonic Neural Network: combining Deep Learning with Domain Knowledge for Chiller Plants Energy Optimization