Modelling of Compressors in an Industrial CO$_2$-Based Operational Cooling System Using ANN for Energy Management Purposes

Abstract

Large scale cooling installations usually have high energy consumption and fluctuating power demands. There are several ways to control energy consumption and power requirements through intelligent energy and power management, such as utilizing excess heat, thermal energy storage and local renewable energy sources. Intelligent energy and power management in an operational setting is only possible if the time-varying performance of the individual components of the energy system is known. This paper presents an approach to model the compressors in an industrial, operational two-stage cooling system, with CO$_2$ as the working fluid, located in an advanced food distribution warehouse in Norway. An artificial neural network is adopted to model the compressors using the operational data. The model is trained with cooling medium evaporation and condensation temperature, suction gas temperature and compressor operating frequency, and outputs electrical power load and cooling load. The best results are found by using a single hidden layer with 45 hidden neurons and a hyperbolic tangent activation function trained with the Adam optimizer, with a resulting mean squared error as low as 0.08% for both the training and validation data sets. The trained model will be part of a system implemented in a real-world setting to determine the cooling load, compressor power load, and coefficient of performance. An intelligent energy management system will utilize the model for energy and power optimization of the cooling system by storing energy in a thermal energy storage, using predictions of energy demand and cooling system performance.