Energy Intelligence & Analytics

| Overview

Energy Intelligence as First-Class Data is a foundational framework introduced to standardize how energy consumption data is detected, calculated, normalized, retained, and exposed across all supported 75F equipment profiles.

Buildings today rely on multiple methods to estimate or measure energy consumption, including:

• External energy meters
• Current transformers (CTs)
• Nameplate data combined with runtime logic

Since each of these methods behaves differently across equipment profiles, energy data becomes inconsistent, difficult to scale, and challenging to use for analytics, reporting, and optimization workflows.

To address this challenge, the CCU introduces a unified Energy Intelligence framework that treats energy as a first-class citizen across all applicable equipment profiles.

| Problem Statement

Different equipment profiles expose and calculate energy information differently, depending on:

• Metering hardware availability
• Current transformer configuration
• Runtime-based estimation logic
• Profile-specific implementations

This creates several platform-level challenges:

• Inconsistent energy representation across profiles
• Lack of a standardized energy consumption point
• Difficulty building scalable analytics and dashboards
• Challenges normalizing units across devices
• Increased integration and maintenance complexity
• Limited support for future optimization and intelligence workflows

A unified framework is therefore required to ensure all equipment profiles follow a common approach for energy handling.

| Objectives

The Energy Intelligence framework aims to:

• Automatically detect supported energy data sources
• Support multiple energy metering mechanisms
• Normalize energy consumption data across equipment profiles
• Provide a standardized total consumption point
• Enable configurable retention and aggregation intervals
• Support future analytics and optimization capabilities
• Simplify downstream reporting and dashboarding

| Supported Energy Source Approaches

The framework supports three primary approaches for energy calculation and monitoring.

• Approach 1 – External Meter Based Energy Monitoring
• Approach 2 – Current Transformer (CT) Based Energy Calculation
• Approach 3 – Nameplate and Runtime Based Energy Estimation

Let us look into the above aprroacehs in detail in the following sections.

| Approach 1 – External Meter-Based Energy Monitoring

Description

This approach supports Modbus and BACnet metering devices connected to the CCU.

If an external equipment connected to the CCU exposes a point containing the following tags:

• energy
• consumption or summation
• bacnet or modbus

Then, energy monitoring is automatically enabled.

The CCU creates the required internal energy point and writes consumption values at the configured interval duration.

Default interval duration:

• 15 minutes

Detection Logic

Energy monitoring starts when a connected point satisfies the following criteria:

• Point tagged with energy
• Point tagged with either consumption or summation
• Point associated with either BACnet or Modbus

Supported Profiles

• All BACnet profiles
• All Modbus profiles
• System level equips
• Zone level equips (PCN)

Benefits

• Direct metered energy acquisition
• High accuracy measurements
• Minimal estimation dependency
• Standardized storage within CCU
• Supports enterprise energy analytics

| Approach 2 – Current Transformer (CT) Based Energy Calculation

Description

This approach calculates energy consumption using Current Transformer (CT) readings connected to analog inputs within a profile.

The CCU derives energy consumption using:

• CT current readings
• Supply voltage
• Power factor
• Adjustment factor

This allows energy estimation for profiles where direct metering devices are not available.

Required Inputs

The following parameters must be configured:

Energy Calculation Formula

The framework converts ampere readings into energy consumption using the following logic:

1.732×SupplyVoltage×AvgAmp×PowerFactor×AdjustmentFactor1.732 \times SupplyVoltage \times AvgAmp \times PowerFactor \times AdjustmentFactor1.732×SupplyVoltage×AvgAmp×PowerFactor×AdjustmentFactor
 

The calculated value is aggregated based on the configured interval duration.

Supported Profiles

Applicable profiles include:

• OAO
• Staged RTU with VFD
• Fully Modulating AHU
• Advanced AHU V2
• SN/HN SSE
• HS/MS/HSS profiles

Benefits

• Enables energy estimation without external meters
• Supports retrofit installations
• Allows scalable energy adoption
• Leverages existing CT infrastructure
• Standardizes CT-based energy calculations

| Approach 3 – Nameplate and Runtime-Based Energy Estimation

Description

This approach estimates energy consumption using:

• Equipment nameplate power consumption values
• Runtime duration of operational stages

The framework multiplies runtime hours with configured nameplate consumption values to estimate total energy consumed.

This is useful for profiles where neither external meters nor CTs are available.

Example Calculation

Nameplate Configuration

Runtime During 15 Minute Interval

Consumption Calculation

((15×5)+(10×10)+(5×10))/60((15 \times 5) + (10 \times 10) + (5 \times 10))/60((15×5)+(10×10)+(5×10))/60
 

Result:

• Total interval energy consumption expressed in kWh

Benefits

• Enables energy visibility even without physical metering
• Utilizes existing runtime logic
• Low hardware dependency
• Supports legacy and lightweight deployments
• Provides normalized energy estimation across profiles