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Energies 2015, 8(8), 8226-8243; doi:10.3390/en8088226

Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms

1
Department of Building & Plant Engineering, Hanbat National University, Daejeon 305-719, Korea
2
Department of Architectural Engineering, Dankook University, Yongin-si 448-701, Korea
3
Digital Media & Communications Research & Design Center, Samsung Electronic, Suwon-si 443-742, Gyeonggi-do, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Hossam A. Gabbar
Received: 4 June 2015 / Revised: 8 July 2015 / Accepted: 24 July 2015 / Published: 5 August 2015
(This article belongs to the Special Issue Energy Conservation in Infrastructures)
View Full-Text   |   Download PDF [704 KB, uploaded 5 August 2015]   |  

Abstract

This study was conducted to develop an artificial neural network (ANN)-based prediction model that can calculate the amount of cooling energy during the setback period of accommodation buildings. By comparing the amount of energy needed for diverse setback temperatures, the most energy-efficient optimal setback temperature could be found and applied in the thermal control logic. Three major processes that used the numerical simulation method were conducted for the development and optimization of an ANN model and for the testing of its prediction performance, respectively. First, the structure and learning method of the initial ANN model was determined to predict the amount of cooling energy consumption during the setback period. Then, the initial structure and learning methods of the ANN model were optimized using parametrical analysis to compare its prediction accuracy levels. Finally, the performance tests of the optimized model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results and the predicted results under generally accepted levels. In conclusion, the proposed ANN model proved its potential to be applied to the thermal control logic for setting up the most energy-efficient setback temperature. View Full-Text
Keywords: setback temperature; cooling energy consumption; artificial neural network; predictive and adaptive controls; accommodation setback temperature; cooling energy consumption; artificial neural network; predictive and adaptive controls; accommodation
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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MDPI and ACS Style

Moon, J.W.; Jung, S.K.; Lee, Y.O.; Choi, S. Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms. Energies 2015, 8, 8226-8243.

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