Monotonic Neural Network: combining Deep Learning with Domain Knowledge for Chiller Plants Energy Optimization
This work addresses energy optimization for chiller plants, which is a domain-specific problem with incremental improvements over prior methods.
The paper tackles the challenge of optimizing energy consumption in chiller plants with limited data by integrating domain knowledge into a deep learning framework, resulting in a method that outperforms existing approaches in experimental tests on a data center cooling system.
In this paper, we are interested in building a domain knowledge based deep learning framework to solve the chiller plants energy optimization problems. Compared to the hotspot applications of deep learning (e.g. image classification and NLP), it is difficult to collect enormous data for deep network training in real-world physical systems. Most existing methods reduce the complex systems into linear model to facilitate the training on small samples. To tackle the small sample size problem, this paper considers domain knowledge in the structure and loss design of deep network to build a nonlinear model with lower redundancy function space. Specifically, the energy consumption estimation of most chillers can be physically viewed as an input-output monotonic problem. Thus, we can design a Neural Network with monotonic constraints to mimic the physical behavior of the system. We verify the proposed method in a cooling system of a data center, experimental results show the superiority of our framework in energy optimization compared to the existing ones.