Data Mapping and Point Normalization

When integrating multiple BAS platforms or building a unified front-end, consistent data mapping and point normalization enable meaningful analytics, reporting, and operations across diverse systems.

Why Normalization Matters

The Problem

Different systems represent the same data differently:

System A: "AHU-1_Supply_Air_Temp" = 72.5°F
System B: "AHU1.SAT" = 22.5°C
System C: "Building1/HVAC/AHU001/SupplyTemp" = 725 (scaled)

Normalization Goals

• Consistency: Same data looks the same everywhere
• Comparability: Can compare across buildings/systems
• Analytics: Feed consistent data to analytics engines
• User experience: Operators see unified interface
• Reporting: Meaningful enterprise reports

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Naming Conventions

Hierarchical Naming Structure

Establish consistent hierarchy:

Site / Building / System / Equipment / Point

Example:
  Campus1/Bldg_A/HVAC/AHU-01/SupplyAirTemp
  Campus1/Bldg_A/HVAC/AHU-01/SupplyFanStatus
  Campus1/Bldg_A/HVAC/VAV-101/ZoneTemp

Naming Standards

Define conventions for:

Equipment Types:

Equipment	Abbreviation
Air Handling Unit	AHU
Variable Air Volume	VAV
Fan Coil Unit	FCU
Chiller	CH
Boiler	BLR
Cooling Tower	CT

Point Types:

Point	Abbreviation
Supply Air Temperature	SAT
Return Air Temperature	RAT
Discharge Air Temperature	DAT
Zone Temperature	ZNT
Setpoint	SP
Status	STS
Command	CMD

Example Naming Convention

Format: [Site]-[Bldg]-[System]-[Equip#]-[PointType]

Examples:
  HQ-A-AHU-01-SAT    (Supply Air Temp)
  HQ-A-AHU-01-SF-STS (Supply Fan Status)
  HQ-A-VAV-101-ZNT   (Zone Temperature)
  HQ-A-VAV-101-DMR-POS (Damper Position)

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Unit Normalization

Standard Units

Define enterprise standard units:

Parameter	Standard Unit	Convert From
Temperature	°F	°C (×1.8+32)
Pressure	"w.c.	Pa (÷249)
Flow	CFM	L/s (×2.119)
Power	kW	HP (×0.746)
Energy	kWh	BTU (÷3412)

Conversion Implementation

Normalization Function:
  Input: Raw value, source unit, target unit
  Process: Apply conversion formula
  Output: Normalized value
  
Example:
  Input: 22.5°C
  Formula: (°C × 1.8) + 32
  Output: 72.5°F

Precision Standards

Define appropriate precision:

Data Type	Precision
Temperature	0.1°
Humidity	1%
Pressure	0.01"
Flow	1 CFM
Energy	0.1 kWh
Position	1%

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Data Type Mapping

Standardized Point Types

Map all points to standard types:

Analog Types:

• Temperature (°F)
• Pressure (inches w.c., psi)
• Flow (CFM, GPM)
• Humidity (%RH)
• Position (%)
• Power (kW)
• Energy (kWh)

Binary Types:

• Status (On/Off)
• Alarm (Active/Normal)
• Command (Start/Stop)
• Mode (Enabled/Disabled)

Enumerated Types:

• Operating Mode (Off/Heat/Cool/Auto)
• Occupancy (Occupied/Unoccupied/Standby)
• Stage (0/1/2/3...)

Enumeration Mapping

Standardize enumeration values:

Source System A:
  Mode: 0=Stop, 1=Heat, 2=Cool, 3=Auto
  
Source System B:
  Mode: "OFF", "HEATING", "COOLING", "AUTOMATIC"
  
Normalized:
  Mode: Off=0, Heat=1, Cool=2, Auto=3

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Tagging and Metadata

Haystack Tagging

Use Project Haystack standard tags:

Point Definition:
  id: @HQ-A-AHU-01-SAT
  dis: "AHU-1 Supply Air Temp"
  point
  sensor
  air
  temp
  discharge
  equipRef: @HQ-A-AHU-01
  unit: "°F"

Essential Metadata

Every point should have:

• id: Unique identifier
• dis: Display name
• unit: Engineering units
• equipRef: Parent equipment
• siteRef: Parent site
• kind: Data type

Equipment Metadata

Equipment Definition:
  id: @HQ-A-AHU-01
  dis: "Air Handler 1"
  equip
  ahu
  siteRef: @HQ-A
  systemRef: @HQ-A-HVAC

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Quality and Status Mapping

Standardized Quality Flags

Map diverse quality indicators to standard:

Source	Standard Flag
Good/Normal	ok
Offline/No-Comm	down
Fault/Error	fault
Override/Forced	override
Disabled	disabled
Unknown	unknown

Quality Propagation

If source quality = down:
  Normalized quality = down
  Stale flag = true
  Last good value = preserved
  Last good time = timestamp

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Time Normalization

Timestamp Standardization

• Store in UTC internally
• Display in local time
• Handle daylight saving
• Consistent format (ISO 8601)

Internal: 2024-01-15T18:30:00Z
Display: 01/15/2024 1:30 PM EST

Time Zone Handling

Site Configuration:
  Site: HQ Campus
  Timezone: America/New_York
  
All timestamps converted to UTC on ingestion
Displayed in site timezone for operators

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Implementation Approaches

Translation Tables

Maintain mapping tables:

Mapping Table:
| Source System | Source Point | Normalized ID |
|---------------|--------------|---------------|
| JCI_NAE_01 | /AHU1/SAT | HQ-A-AHU-01-SAT |
| Niagara_01 | slot:/AHU1/SupplyTemp/out | HQ-A-AHU-01-SAT |

Semantic Layer

Abstract data access:

User Query: "Get supply air temps for Building A"

Semantic Layer:
  1. Find all points with tag: temp + discharge + air
  2. Filter by siteRef = Building A
  3. Return normalized values

ETL Process

Extract-Transform-Load for analytics:

Extract: Pull from source systems
Transform: Apply normalization rules
Load: Store in analytics database

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Validation and QA

Data Validation Rules

Temperature:
  Min: -40°F
  Max: 150°F
  Rate of change: <10°F/min
  
If validation fails:
  Flag quality = suspect
  Log validation error
  Use last good value

Completeness Checks

• Monitor for missing data
• Track communication health
• Report data gaps
• Alert on stale data

Cross-Reference Validation

Validation Rule:
  Supply Temp > Return Temp in cooling mode
  If not: Flag as suspect, investigate

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Documentation Requirements

Mapping Documentation

Maintain current documentation:

• Source-to-normalized mappings
• Conversion formulas
• Tag definitions
• Exception handling

Data Dictionary

Point: HQ-A-AHU-01-SAT
  Description: AHU-1 Supply Air Temperature
  Data Type: Analog
  Unit: °F
  Range: 40-90
  Source: JCI NAE-01
  Source Point: /AHU1/SAT
  Conversion: None
  Tags: temp, discharge, air, sensor

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Best Practices

Design Phase

1. Define naming convention first
2. Establish standard units
3. Choose tagging schema
4. Plan quality handling
5. Document everything

Implementation

1. Validate mappings thoroughly
2. Test with real data
3. Verify conversions
4. Check quality propagation
5. Train users

Maintenance

1. Review unmapped points
2. Update for system changes
3. Audit data quality
4. Refine validation rules
5. Maintain documentation

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References

• Project Haystack: https://project-haystack.org
• Brick Schema: https://brickschema.org
• ASHRAE 223P: Semantic Data Model
• Industry naming conventions

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Good data normalization is invisible to end users—they just see consistent, meaningful data. The work happens in careful planning and thorough mapping.