Deep neural networks can play a role in increasing the cooling efficiency of data centers. The trained model performs poorly with both the trained dataset and the tested dataset.Ĭreating a PUE model using a neural network: Neural networks are a set of machine learning algorithms that can simulate the cognitive behavior of interactions between neurons. If too few parameters are found, underfitting occurs. The trained model will have a better fit from the trained data than from the tested data, but it has poor generalizability.
Too many parameters will lead to overfitting. Selecting too many or too few parameters will affect the accuracy of the final model. Second, feature engineering is performed on large amounts of raw data to identify the key parameters that affect PUE. The key technologies used in this solution include:īig data collection: Given the complexity of data center cooling systems, information about the power supply system, cooling system, and environment parameters must be collected.ĭata governance and feature engineering: First, a mathematical tool is used to perform data governance on the raw data collected, providing high-quality data for subsequent model training. Powered by AI and big data technologies, Huawei's solution enables smart cooling systems for data centers. With the PUE value, the data center can make optimizations as expected based on the current climate and load conditions to achieve the energy-saving target. AI can be used to determine the relationships between the PUE and the data of different features and then predict a PUE value. However, relying on experience that varies between team members doesn't always result in accuracy.įor a complex chilled water cooling system, a new control algorithm is needed to achieve overall optimal performance. Based on their experience, the team determines how to adjust the parameters of a cooling system for different seasons, ambient temperatures, and load rates to maximize the energy efficiency of the cooling system. Traditional O&M depends on an experienced O&M team.
However, energy-efficient hardware does not necessarily result in the most energy savings because energy efficiency is closely related to the O&M of a data center. The chilled water cooling system of a data center saves energy in two ways: design and O&M.Įnergy-saving through design comes from designing the right cooling systems and selecting the right equipment, which focuses on using hardware to save energy.
The solution further reduces the energy consumption of data centers while enabling smart cooling of large data centers and cutting PUE. Based on its extensive experience in data center construction, Huawei launched the solution powered by big data and AI.
0 kommentar(er)
