As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
This study aims to provide a machine learning approach to predict the performance of Ground Coupled Heat Pumps (GCHPs) with horizontal Ground Heat Exchangers (GHEs). Specifically, an ANN model was developed for this purpose which can potentially be generally applied to similar sites at different locations and climate conditions, with even limited types of input data. In this example, a TRNSYS model regarding a typical horizontal trench within a rural farm in Australia, has been developed and verified, covering over 50 different yearly loading patterns under 3 different climate conditions. The simulated performance data is then used to train the artificial neural network. As results, the trained ANN is able to predict the performance of GSHPs systems with identical GHEs even under climatic conditions (and locations) that has not been specifically trained for. With only limited input data, the presented ANN shows no more than 5% error in most cases tested.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.