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  • BHC3 Energy Management

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

    BHC3 Energy Management

    Reduce whole-facility energy costs for refineries

    Reduce Overall Energy Costs and Improve Building Operations

    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.


    Streaming energy analytics

    Streaming energy analysis

    Develop high-level and granular insights into energy trends in energy assets using configurable KPIs, benchmarking and time series visualizations.
    Peak demand forecasting

    Peak-demand forecasting

    Predict peak loads in energy assets with advanced AI algorithms that use streaming energy data, building data (e.g., lighting sensors, audits, operating schedules) and weather data.
    End use disaggregation

    End-use disaggregation

    Conduct granular energy analysis of energy assets with AI-based algorithms that disaggregate consumption to identify constituent end-use loads such as heating, cooling and lighting within the facility.
    Building optimization

    Whole-building optimization

    Optimize whole-building energy costs, maintain comfort and make effective use of on-site power supply (e.g., solar) using AI-enabled techniques.
    Anomaly detection

    Anomaly detection

    Use AI algorithms to detect operational anomalies and billing errors relating to energy assets.
    Operator engagement

    Operator engagement

    Use AI-enabled segmentation, energy analytics, savings recommendations and alerts to spur action that saves energy and improves overall operations of energy assets.
    Measurement verification

    Measurement and verification

    Track and report energy savings of energy assets using machine learning algorithms.
    Project analyzer

    Project analyzer

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

    Virtual building audit

    Enhance accuracy of AI models for buildings and enable new analytics by collecting cross-facility behavioral, operational and building-characteristic data.
    Power purchase analysis

    Power purchase analysis

    Evaluate real-time power demand, on-site energy supply, utility tariffs and market pricing for actionable insights into cost reduction opportunities.
    Self-service data science

    Self-service data science

    Visually create whole-facility analytics and machine learning models. Analyze, explore and derive business insights quickly, all without writing a single line of code.


    Integrate energy asset data from any enterprise system, third-party source, building system or on-site generation source. Embed insights into existing applications using APIs.



    Reduce energy costs from energy assets by 15 to 30% using predictive analytics to identify high-impact energy saving opportunities and operational improvements.


    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.


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


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


    Rapidly deploy and configure energy solutions using self-service tools for AI, analytics, dashboards and data integrations.

    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., building audits, weather).

    This unified data set in BHC3™ AI Suite enables multi-dimensional energy analysis, predictive analytics, building optimization and anomalous performance monitoring across numerous energy asset classes. BHC3 Energy Management processes energy asset data in near real time, performing continuous analyses, generating insights, and delivering recommendations through multi-channel solutions such as mobiles alerts, email reports and control signals directly to building equipment.

    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


    Proven results in weeks, not years

    Get insights into BHC3 capabilities, enterprise AI best practices and highest-value use cases.
    Understand BHC3™ AI Suite's capabilities, its model-driven architecture and test it against your company's sample data set.
    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.

    Thank you for your interest in BHC3.ai

    We'll review your information and a team member will get back to you within 24-48 hours.

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