A combined neural and genetic algorithm model for data center temperature control

As many parameters of the cooling system in data center mainly depend on manual control, it is urgent to fix the problem of simulation and optimization at the same time and develop effective mitigation methods. In this paper, we proposes a combined method based on machine learning. Firstly, we have built the neural network with the temperature and humidity of the equipment in real data, then established evaluation model, and finally the optimal setting has been obtained by a genetic algorithm. Our results show that that the effect is better than artificial method and traditional greedy algorithms with 3% - 15% relative reduction in errors of temperature and humidity per set of parameters.