降低facility-wide energy costs for factories in the energy sector
减少炼油厂的整体设备能源成本
BHC3™ Energy Management uses machine learning to help energy enterprises gain visibility into their cross-facility energy expenditures and prioritize actions to reduce their overall operational costs. The application leverages advanced AI and optimization algorithms to model building operations, detect anomalies in refinery assets, predict energy savings opportunities, and help energy facility managers take action in near real time.
使用预测分析将能源资产从能源资产降低能源成本,以识别高影响力的节能机会和运营改进。
Forecast energy demand in energy assets with greater accuracy using tailored machine learning analytics that achieve greater than 80% accuracy.
Increase CapEx investment ROI by optimizing investment in building and energy infrastructure (e.g., solar, smart lighting, energy storage, EVs).
Automate energy facility management with streaming analytics and AI-algorithms that predict loads of energy assets to dynamically optimize building operations.
提高reliability of energy assets by integrating on-site power, predicting peak and outage events, and optimizing demand across buildings.
精简reporting of energy asset power usage for quarterly/annual reviews and financial audits.
使用用于AI,Analytics,Dashboard和Data Integations的自助式工具迅速部署和配置能源解决方案。
BHC3 Energy Management creates a unified federated cloud image of energy asset data from all key sources, including energy data (e.g., meter readings, utility bills), site operational data (e.g., schedules, occupancy), telemetry signals from building systems (e.g., lighting, HVAC), and third-party data (e.g.,建立审计,天气)。
此统一数据集BHC3™AI套房通过多次能源资产类别实现多维能量分析,预测分析,建筑优化和异常性能监测。BHC3能源管理在近实时处理能源资产数据,执行连续分析,通过多通道解决方案,如移动警报,电子邮件报告和控制信号直接到建筑设备的控制信号进行推荐。
With a comprehensive view of data across many energy systems and AI-based algorithms running continuously at scale, BHC3 Energy Management empowers facilities managers to optimize building operations, reduce utilities expenditure and achieve sustainability objectives.
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