Abstract
In this study, an artificial neural network (ANN) was used to model the thermal performance of a novel direct-expansion solar-assisted sky-source heat pump (SSHP) during winter. The input parameters of the ANN take into account the weather conditions, water loop characteristics, and the compressor characteristics of the SSHP. The following four output parameters were adopted to evaluate the SSHP performance: the outlet water temperature of the water loop, electricity consumption, heat production, and the coefficient of performance. To increase the accuracy of the ANN and simultaneously investigate the effects of each of the input parameters on the performance of the SSHP, the combination of input parameters for the validation data set was varied in multiple case studies. Additionally, learning curves were introduced to clarify the relationship between the training data size and the generalization performance of the ANN. Finally, the ANNs with the best performance were selected and evaluated based on the test data set by using metrics such as the root mean square error. The reported results demonstrated that the ANN model has comparatively high SSHP winter performance prediction accuracy.
Highlights
Renewable energy has recently been receiving an increasing amount of attention because of increasing energy needs and the need to reduce greenhouse gas emissions [1]
We considered the sky-source heat pump (SSHP) to be a black box that is influenced by the ambient environment and its own characteristics in order to determine the input and output parameters of the Artificial neural network (ANN) model
To maximize accuracy of the ANN model and investigate the effects of the input parameters on the performance of the SSHP, case studies with different combinations of input parameters were carried out and evaluated in terms of the following metrics: R2, root mean squared error (RMSE), and mean absolute error (MAE), which were calculated as based on the validation data set
Summary
Renewable energy has recently been receiving an increasing amount of attention because of increasing energy needs and the need to reduce greenhouse gas emissions [1]. There are multiple types of renewable energies that can be exploited as thermal resources for buildings, such as solar radiation and ground heat. Based on well-known information, we developed a multiple-source and multiple-use heat pump system (MMHP) system This is a distributed water-source heat pump system that can utilize various types of renewable energies surrounding a building to meet a variety of thermal demands [2]. Gunasekar et al developed an ANN to predict the energy performance of a photovoltaic-thermal evaporator used in solar assisted heat pumps and applied analysis of variance to identify the significant ambient parameter influencing the energy performance. In this study, an ANN was constructed based on the large amount of data collected from the previous winter field experiment, and subsequently used to model the performance of an SSHP.
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