Application of artificial neural networks for determining energy-efficient operating set-points of the VRF cooling system. (15th November 2017)

• Record Type: Journal Article
• Authors: Chung, Min Hee; Yang, Young Kwon; Lee, Kwang Ho; Lee, Je Hyeon; Moon, Jin Woo
• Is Part Of: Building and environment. Volume 125(2017)
• Page Start: 77
• Page End: 87
• Publication Date: 2017-11-15
• Subjects: Artificial neural network -- Predictive controls -- Refrigeration evaporation temperature set-point -- Supply air temperature set-point -- Condenser fluid temperature set-point -- Condenser fluid pressure set-point; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2017.08.044
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: The aim of this study was to develop an Artificial Neural Network (ANN) model that can predict the amount of cooling energy consumption for the different settings of the variable refrigerant flow (VRF) cooling system's control variables. Matrix laboratory (MATLAB) and its neural network toolbox were used for the ANN model development and test performance. For the model training and performance evaluation, data sets were collected through the field measurement. Four steps were conducted in the development process: initial model development, input variable selection, model optimization, and performance evaluation. In the initial model development and input variable selection process, seven input variables were selected as input neurons: TEMPOUT, HUMIDOUT, TEMPIN, LOADCOOL, TEMPSA, TEMPCOND, and PRESCOND . In addition, the initial model was optimized to have 2 hidden layers, 15 hidden neurons in each hidden layer, a learning rate of 0.3, and a momentum of 0.3. The optimized model demonstrated its prediction accuracy within the recommended level, thus proved its potential for application in the control algorithm for creating a comfortable indoor thermal environment in an energy-efficient manner. Highlights: Predictive and adaptive ANN model was developed for the cooling system. The model predicted cooling energy consumption for the different variable settings. Model optimization was conducted for the accurate and stable prediction. The optimized model demonstrated its prediction accuracy within the recommended level.