MONOTONIC NEURAL NETWORK: COMBINING DEEP LEARNING WITH DOMAIN KNOWLEDGE FOR CHILLER PLANTS ENERGY OPTIMIZATION

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 realworld 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.

1. INTRODUCTION

The demand for cooling in data centers, factories, malls, railway stations, airports and other buildings is rapidly increasing, as the global economy develops and the level of informatization improves. According to statistics from the International Energy Agency (IEA, 2018), cooling energy consumption accounts for 20 of the total electricity used in buildings around the world today. Therefore, it is necessary to perform refined management of the cooling system to reduce energy consumption and improve energy utilization. Chiller plants are one of the main energy-consuming equipment of the cooling system. Due to the non-linear relationship between parameters and energy consumption, and performance changes due to time or age, deep learning is very suitable for modeling chiller plants. In recent years, deep learning (Goodfellow et al., 2016) research has made considerable progress, and algorithms have achieved impressive performance on tasks such as vision (Krizhevsky et al., 2012; He et al., 2016) , language (Mikolov et al., 2011; Devlin et al., 2018) , and speech (Hinton et al., 2012; Oord et al., 2016) , etc. Generally, their success relies on a large amount of labeled data, but real-world physical systems will make data collection limited, expensive, and low-quality due to security constraints, collection costs, and potential failures. Therefore, deep learning applications are extremely difficult to be deployed in real-world systems. There are some researches about few sample learning summarized from Lu et al. (2020) , which focusing on how to apply the knowledge learned in other tasks to few sample tasks, and applications in computer vision, natural language processing, speech and other tasks. Domain Knowledge that has been scientifically demonstrated, however, is more important in few sample learning tasks, especially in the application of physical system optimization. Domain knowledge can provide more derivable and demonstrable information, which is very helpful for physical system optimization tasks that lack samples. We discussed the method of machine learning algorithms combined with domain knowledge and its application in chiller energy optimization in this article.

