Development and validation of a heat pump system model using artificial neural network

Abstract

Modeling of direct expansion (DX) air conditioning and heat pump systems can be necessary in developing energy saving methods required to reduce energy consumption in buildings. The artificial neural networks (ANN) can be simple and reliable as compared to traditional methods. A properly trained artificial neural network can provide accurate results, while being relatively straightforward and easy in development. This paper discusses the implementation and validation of an artificial neural network modeling technique to predict the performance of a DX air conditioning and heat pump. The model predicts the compressor power as a function of airflow rate, humidity ratio, ambient and mixed air temperatures. Three different leaning algorithms were compared and validated versus the actual data using statistical indexes to determine the most accurate model structures. Experiments were conducted on a 3-ton DX split-system heat pump fully implemented. The heat pump ran over the course of several months to obtain a wide range of measurements. The results showed that artificial neural network can provide very accurate predictions and this ANN model technique can effectively be used for many energy-efficiency heat pump applications.