Development of artificial neural network models for indirect evaporative coolers in multi-climate regions based on field measurement

Data-driven artificial neural networks (ANN) based on data drivers with their powerful self-learning capability and high adaptability are gaining increasing attention for the application in modeling indirect evaporative coolers (IEC). However, most ANN models of IEC in existing studies are either limited to a specific climate region or conventional IEC configurations. In this paper, a multi-region back-propagation (BP) neural network model for predicting the performance of advanced dew-point IECs is developed. Operational data for ANN model development were collected through field measurements in IEC projects in three typical climate zone cities in China (Dunhuang, Yulin, and Fuzhou), covering arid, moderately wet, and humid regions. A comparative study of three single-region neural network models was conducted in terms of both convergence characteristics and statistical performance metrics. Each model contains two input variables (inlet air temperature, inlet air humidity) and one output variable (wet-bulb efficiency). The results show that the model fits best in Yulin, followed by Dunhuang and Fuzhou, with total correlation coefficients of 0.9918, 0.9477, and 0.8946, respectively. The predicted values of wet-bulb efficiency are in good agreement with the actual operating data, and the deviation of almost all predicted values is within ±10%.