By leveraging AI technology and HVAC expertise, the "AI-based energy-saving optimization system of data center" integrates multiple aspects such as building automation (BA), power, and dynamic environment monitoring to achieve precise, dynamic, on-demand cooling, centralized management, optimal control, and automatic adjustments for the cooling system. This effectively reduces the energy consumption of the cooling system, ultimately lowering the overall PUE index of the data center.
Core Value
All-in-one aggregation:
• Breaks the "information silos" of independent operation of professional subsystems to form an integrated, full-link energy management and control data platform, and builds a digital base for energy efficiency analysis and energy-saving optimization
Refined analysis:
• Multi-dimensional, multi-view energy consumption display and energy efficiency analysis, refined insights into energy destination, and identification of energy-saving space based on energy consumption distribution
Algorithm strategy:
• The integrated AI-based algorithm model conducts systematic, end-to-end operational diagnosis based on real-time operating parameters, and develops control strategies to improve energy efficiency
Safe automatic control:
• The energy-saving strategy is sent to the equipment via professional systems such as BA and dynamic environment monitoring. The automatic control of the HVAC system is implemented on the basis of ensuring the operation safety, while the real-time optimisation of PUE is achieved
Continuous optimization:
• AI model training and AI data inference are conducted with accumulated operating data under different operating conditions, to improve the accuracy and adaptability of the AI-based algorithm model and enable the continuous optimal operation of the refrigeration system.
Application Scenarios
Medium to large/super large data centers with (water/air cooled) chilled water systems.
Functions
• Data management
Data management, consolidation and analysis based on a unified object model and professional data requirements, to build a database for energy efficiency management and energy-saving optimization
• Topology display
Electric and HVAC system topologies are mapped according to the actual on-site conditions of the data center with the configuration tools to help present the equipment operation status, key operating parameters and the information of energy indicators and provide users with a full-scene and full-link energy board
• Alarm management
Multi-dimensional alarm trigger conditions, including thresholds, changes, actions and trends are provided to support the setting of multi-level, multi-threshold alarm and templated batch alarm rules, helping users to keep up with energy use emergencies in time
• Energy efficiency management
Multi-dimensional energy efficiency monitoring, analysis and display functions of space, subsystems and equipment are configured to generate early warning based on trend analysis, support the accounting of electricity consumption cost, and provide data support for green energy-saving operation of the data center
• Energy-saving optimization
The prefabricated AI-based energy-saving algorithm model library, through the management, analysis and diagnosis of real-time operating data, gains an insight into energy-saving space, generate optimization strategies, and adopt automatic/manual control modes to achieve strategy distribution and consequently achieve dynamic optimisation of the HVAC system of the data center
• Statement management
The user-defined statement styles are supported to help users analyze energy utilization from multiple dimensions and multiple perspectives;
• Authority management
The authority management can be implemented for roles and workgroups to support independent or centralized authorization to users.
