Abstract
The performance of various multilayer neural network algorithms to predict the energy consumption of an absorption chiller in an air conditioning system under the same conditions was compared and evaluated in this study. Each prediction model was created using 12 representative multilayer shallow neural network algorithms. As training data, about a month of actual operation data during the heating period was used, and the predictive performance of 12 algorithms according to the training size was evaluated. The prediction results indicate that the error rates using the measured values are 0.09% minimum, 5.76% maximum, and 1.94 standard deviation (SD) for the Levenberg–Marquardt backpropagation model and 0.41% minimum, 5.05% maximum, and 1.68 SD for the Bayesian regularization backpropagation model. The conjugate gradient with Polak–Ribiére updates backpropagation model yielded lower values than the other two models, with 0.31% minimum, 5.73% maximum, and 1.76 SD. Based on the results for the predictive performance evaluation index, CvRMSE, all other models (conjugate gradient with Fletcher–Reeves updates backpropagation, one-step secant backpropagation, gradient descent with momentum and adaptive learning rate backpropagation, gradient descent with momentum backpropagation) except for the gradient descent backpropagation model yielded results that satisfy ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) Guideline 14. The results of this study confirm that the prediction performance may differ for each multilayer neural network training algorithm. Therefore, selecting the appropriate model to fit the characteristics of a specific project is essential.
Highlights
Buildings consume most of their energy during the operating phase of their entire service life
Previous studies used a single machine learning algorithm, but this study evaluated the predictive performance of each algorithm using various algorithms classified as multilayer shallow neural networks among deep learning neural network techniques to predict the energy consumption of absorption heat pumps
We examined whether prediction results that meet the criteria of ASHRAE guideline 14 can be obtained when training shallow natural network models with a small amount of data (251 datasets)
Summary
Buildings consume most of their energy during the operating phase of their entire service life. Cheng et al used an artificial neural network (ANN) model to investigate building envelope performance, parameters, heating degree day, and cooling degree day as input variables and were able to increase the prediction accuracy by more than 96% compared to the existing method [4]. It was reaffirmed that the artificial neural network-based prediction model could obtain relatively high accuracy prediction results with only a sufficient amount of data [20] Based on this earlier work, the energy consumption and load predictions based on ANNs were conducted to develop an energy management technique for centralized air conditioning systems. The predictive performance of 12 multilayer shallow neural network training algorithms was evaluated using energy consumption data of the heating period absorption heat pump in the actual building.
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