Comparative Evaluation of Predicting Energy Consumption of Absorption Heat Pump with Multilayer Shallow Neural Network Training Algorithms

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.

Managing energy performance through heat pumps. Success drivers and barriers in residential sector

“…Thus, homeowners can improve the energy performance of their homes and contribute to a more sustainable future. The value of reducing energy consumption in buildings has increased worldwide, so the adoption of energy efficiency techniques during the construction and operation of office and residential buildings would play an important role in the creation of sustainable cities in the future (Kim et al, 2022). Energy performance and efficiency in the home is a major concern for households.…”

“…Decent remuneration for apprenticeships and public funding of retraining schemes are important factors in attracting new users and citizens who implement technology. Governments can also play an important role in promoting training for heat pump installation to potentially induce a positive perception (Kim et al, 2022). Thus, we hypothesize the following:…”

Optimization of the ANN Model for Energy Consumption Prediction of Direct-Fired Absorption Chillers for a Short-Term

“…For the ANN model, the Neural Networks Toolbox of MATLAB (R2020a) was used to create a neural network. The NARX Feedforward Neural Networks model is a multi-layer perceptron ANN model that consists of an input layer, a hidden layer, and an output layer [26]. In addition, the Levenberg-Marquardt algorithm was used to find the minimum of a function over a space of parameters, which is a popular trust algorithm.…”

“…In addition, the Levenberg-Marquardt algorithm was used to find the minimum of a function over a space of parameters, which is a popular trust algorithm. A similar structure of the ANN model used in the previous studies was used for the present study, and the structure is shown in Figure 2 [26,27].…”