宝腾观澜数据中心冷源站节能优化系统模型数据

宝腾观澜数据中心冷源站节能优化系统模型数据是通过历史数据训练AI模型，寻找最优参数，并通过冷机群控优化、供水温度优化控制、冷冻水泵优化控制、冷却水泵优化控制、冷却塔优化控制的手段达到挖掘节能空间，降低能耗成本的目标。基于该模型，可以实现：全闭环控制，无人值守；节能逼近历史极限；5分钟验证效果。该模型数据对仍采用人工经验本地控制冷机群、供水温度、冷冻水泵、冷却水泵、冷却塔的数据中心来说是具有极大的参考价值的，以物理框架+数据驱动的深度学习算法模型取代人工经验对数据中心的节能减排具有极大帮助。此外，该模型数据对节能公司、自控公司、集成公司、设备公司等完善自身节能算法、提高节能服务水平也具有极大借鉴作用。

The model data of the energy-saving optimization system for the cold source station of Baoteng Guanlan Data Center is developed by training an AI model on historical data to identify optimal parameters. It achieves the objectives of tapping energy-saving potential and reducing energy consumption costs through optimized controls for chiller groups, supply water temperature, chilled water pumps, cooling water pumps, and cooling towers. Based on this model, three core capabilities are enabled: fully closed-loop control with unattended operation; energy savings approaching historical limits; and effect verification within 5 minutes. This model data holds substantial reference value for data centers that still adopt manual experience-based local control over chiller groups, supply water temperature, chilled water pumps, cooling water pumps, and cooling towers. Replacing manual experience with a physical framework + data-driven deep learning algorithm model can greatly facilitate energy conservation and emission reduction in data centers. Furthermore, this model data also offers valuable reference for energy-saving companies, automation companies, system integration companies, equipment manufacturers and other enterprises to optimize their own energy-saving algorithms and improve their energy-saving service levels.

搜集汇总

数据集介绍

特点

该数据集为宝腾观澜数据中心冷源站节能优化系统模型数据，通过历史数据训练AI模型，优化冷机群控、供水温度等参数，实现节能减排目标。数据集包含算法模型信息和训练预测数据，适用于数据中心节能优化和相关企业算法改进。