Abstract: Amid the information era, energy consumption of IDC Computer Room Air Conditioning (CRAC) system is becoming increasingly serious. Thus there is growing concern over energy saving and consumption reduction. Based on the analysis of the energy saving application of the air conditioning system in the present computer room, a new energy saving method of the IDC CRAC system, which presents energy saving decision based on the prediction of temperature, is proposed. Its principle is the collection of CPU utilization reflected the change of equipment working load, the temperature in hot spots and cold area. Then, to build a BP Neural Network model, taking the working load and the temperature in hot spots for the actual input, taking the temperature in a cold area for actual output. The BP Neural Network model can predict the temperature in hot spots of the next, when a set of real-time data into the model. Choosing a reasonable and effective decision-making scheme of the air conditioning system can realize energy saving control. Preliminary simulation results show, through the establishment of BP network model obtain approximation error of training samples and the prediction error of testing samples, both to highlight the advantages of the model. Finally, the distribution of temperature change about CRAC system whole day obtained by simulation shows that the proposed energy-saving method can reduce the energy consumption of IDC, fully embodies the effect of energy saving.

Zou Yan, WU Fei, Xing Jian and Dong Bo, “Research on Energy Saving Method for IDC CRAC System based on Prediction of Temperature” International Journal of Advanced Computer Science and Applications(IJACSA), 6(8), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060824

@article{Yan2015,
title = {Research on Energy Saving Method for IDC CRAC System based on Prediction of Temperature},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2015.060824},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060824},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {8},
author = {Zou Yan and WU Fei and Xing Jian and Dong Bo},
}