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Energies 2015, 8(10), 10775-10795; doi:10.3390/en81010775

ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms

1
School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
2
DMC R&D Center, Samsung Electronic, Suwon-si 443-742, Gyeonggi-do, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Hossam A. Gabbar
Received: 15 June 2015 / Revised: 21 September 2015 / Accepted: 23 September 2015 / Published: 28 September 2015
(This article belongs to the Special Issue Energy Conservation in Infrastructures)
View Full-Text   |   Download PDF [945 KB, uploaded 28 September 2015]   |  

Abstract

This study aimed at developing an artificial-neural-network (ANN)-based model that can calculate the required time for restoring the current indoor temperature during the setback period in accommodation buildings to the normal set-point temperature in the cooling season. By applying the calculated time in the control logic, the operation of the cooling system can be predetermined to condition the indoor temperature comfortably in a more energy-efficient manner. Three major steps employing the numerical computer simulation method were conducted for developing an ANN model and testing its prediction performance. In the development process, the initial ANN model was determined to have input neurons that had a significant statistical relationship with the output neuron. In addition, the structure of the ANN model and learning methods were optimized through the parametrical analysis of the prediction performance. Finally, through the performance tests in terms of prediction accuracy, the optimized ANN model presented a lower mean biased error (MBE) rate between the simulation and prediction results under generally accepted levels. Thus, the developed ANN model was proven to have the potential to be applied to thermal control logic. View Full-Text
Keywords: temperature controls; thermal comfort; artificial neural network; predictive controls; accommodations temperature controls; thermal comfort; artificial neural network; predictive controls; accommodations
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Moon, J.W.; Kim, K.; Min, H. ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms. Energies 2015, 8, 10775-10795.

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