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

TL;DR

This paper introduces a domain knowledge-based deep learning framework with monotonic constraints to optimize energy consumption in chiller plants, effectively addressing small sample sizes and physical system constraints.

Contribution

It proposes a novel monotonic neural network that incorporates physical domain knowledge for energy optimization in chiller systems, improving performance over existing methods.

Findings

Outperforms existing energy optimization methods in experiments

Effectively models physical monotonic relationships in chiller systems

Reduces model complexity with domain-informed neural network design

Abstract

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.