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.
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.
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）。
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.