Treffer: Smart Performance Evolution of a Solar Water Heating System with PCM by Using Deep Learning Approach.

The solar water heating business by itself accounts for approximately 80% of the total market for solar thermal energy. The construction sector all around the world has discovered several applications for solar water heating over the course of the past few decades. In this paper, we develop a deep learning approach for a solar water heating system built with Phase-change materials. The results demonstrate that the proposed Phase-change materials and deep neural network model outperforms other methods in terms of accuracy and cost reduction. The use of Phase-change materials in solar water heating systems offers advantages such as high energy storage density and isothermal phase transitions. This research builds upon previous studies that have investigated Phase-change materials applications in solar heating systems. The proposed method utilizes deep neural network and back propagation algorithms to develop an accurate model for predicting solar performance. The model's accuracy is evaluated using metrics like Root Mean Square Error and Mean Absolute Percentage Error. The simulation is conducted in terms of different variables in python to test the efficacy of the model. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Components & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)