BHC3 Energy Management

降低facility-wide energy costs for factories in the energy sector

BHC3 Energy Management

减少炼油厂的整体设备能源成本

降低整体能源成本并改善建筑运营

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.

Features

流媒体能量分析

流能量分析

使用可配置的KPI,基准测试和时间序列可视化,在能源资产中的能源趋势中开发高级和颗粒洞察力。
峰值需求预测

峰值需求预测

预测高级AI算法中的能量资产中的峰值负载,该算法使用流能量数据,建立数据(例如,照明传感器,审计,操作时间表)和天气数据。
最终使用分类

End-use disaggregation

用基于AI的算法进行能量资产的颗粒能量分析,该算法分解消耗以确定设施内的加热,冷却和照明等组成最终使用载荷。
建筑优化

Whole-building optimization

优化全建筑能源成本,维护舒适性,并使用能够使用AI的技术进行现场电源(例如,太阳能)。
Anomaly detection

Anomaly detection

Use AI algorithms to detect operational anomalies and billing errors relating to energy assets.
运营商订婚

运营商订婚

使用启用AI的分段,能源分析,节省储蓄建议和警报,以促使能源节省能源并提高能源资产的整体运营。
测量验证

Measurement and verification

使用机器学习算法跟踪和报告能源资产的节能。
Project analyzer

Project analyzer

Assemble, prioritize and manage a portfolio of energy facility capital projects that maximize financial objectives.
Virtual building analytics

虚拟建筑审计

Enhance accuracy of AI models for buildings and enable new analytics by collecting cross-facility behavioral, operational and building-characteristic data.
电力购买分析

电力购买分析

评估实时电力需求,现场能源供应,公用事业资费和市场定价,以获得可行的洞察成本降低机会。
Self-service data science

Self-service data science

在视觉上创建整个设施分析和机器学习模型。快速分析,探索和派生商业洞察力,无论都不撰写单线代码。
Interoperability

Interoperability

将能源资产数据集成到任何企业系统,第三方来源,建筑系统或现场生成源。使用API​​将见解嵌入到现有应用程序中。

福利

降低

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

Forecast energy demand in energy assets with greater accuracy using tailored machine learning analytics that achieve greater than 80% accuracy.

Increase

Increase CapEx investment ROI by optimizing investment in building and energy infrastructure (e.g., solar, smart lighting, energy storage, EVs).

Automate

Automate energy facility management with streaming analytics and AI-algorithms that predict loads of energy assets to dynamically optimize building operations.

提高

提高reliability of energy assets by integrating on-site power, predicting peak and outage events, and optimizing demand across buildings.

精简

精简reporting of energy asset power usage for quarterly/annual reviews and financial audits.

Deploy

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。

Data Sources

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.

Model-driven architecture for BHC3 Energy Management

演示

经过验证的结果,在几周内,不是几年

时间线
对BHC3功能进行了解,企业AI最佳实践和最高价值用例。
了解BHC3™AI Suite的功能,其模型驱动的架构并对公司的示例数据集进行测试。
Identify a high-impact business problem and collaborate with the BHC3 team to rapidly build an AI application that solves it.
Scale and deploy a tested BHC3 application into production. Incorporate user feedback and optimize algorithms to drive maximum economic value.

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