Phase 3 - Protocol

Phase 3 of WIA-BEMS elevates building energy management from simple data access and control to sophisticated, coordinated workflows. While Phases 1 and 2 established what data looks like and how to access it, Phase 3 defines how systems communicate and coordinate to achieve complex energy management objectives.

This chapter explores the protocol specifications that enable real-time monitoring, automated control sequences, predictive maintenance, fault detection and diagnostics (FDD), and energy optimization algorithms. We'll see how these standardized protocols transform buildings from passive structures into intelligent, responsive systems.

Understanding Protocol vs. API

Before diving into specific protocols, it's important to understand how Phase 3 builds upon Phase 2.

APIs: Individual Transactions

Phase 2 APIs enable individual transactions: "Get energy consumption for last hour," "Set temperature to 22°C," "Retrieve equipment status." Each API call is independent, stateless, and focused on a specific resource or action.

Protocols: Coordinated Workflows

Phase 3 protocols define sequences of interactions that accomplish complex objectives: "Monitor building conditions, predict occupancy, adjust HVAC 30 minutes before arrival, verify comfort levels, log performance." Protocols orchestrate multiple API calls, maintain state across interactions, and implement business logic that spans devices and systems.

Real-Time Monitoring Protocol

The real-time monitoring protocol enables continuous observation of building energy systems with automated alerting and response.

Stream Subscription Pattern

Instead of polling APIs repeatedly, monitoring systems subscribe to data streams and receive updates as they occur.

// MQTT subscription pattern
Topic: buildings/BLDG-001/energy/meters/METER-E-301/power
QoS: 1 (at least once delivery)

// Published messages
{
  "timestamp": "2025-01-15T14:30:00.000Z",
  "power_kw": 150.25,
  "power_factor": 0.92,
  "voltage_v": 480,
  "quality": "verified"
}

// Alert generation based on thresholds
Topic: buildings/BLDG-001/alerts/high-demand

{
  "alert_id": "alert-12345",
  "severity": "warning",
  "type": "demand_threshold_exceeded",
  "triggered_at": "2025-01-15T14:30:00Z",
  "meter_id": "METER-E-301",
  "current_power_kw": 205.3,
  "threshold_kw": 200.0,
  "duration_seconds": 300,
  "recommendations": [
    "Consider load shedding non-critical equipment",
    "Review HVAC setpoints for optimization"
  ]
}

Monitoring Configuration

Parameter	Type	Description	Example
sample_interval	Duration	How often to sample/publish data	1s, 30s, 5m
aggregation_method	Enum	How to aggregate samples	mean, max, min, sum
threshold	Numeric	Alert trigger level	200.0 kW
threshold_duration	Duration	How long above threshold before alert	5m (prevents transient alerts)
alert_recipients	Array	Who receives alerts	["ops@example.com", "sms:+1555..."]

Automated Control Sequences

Control sequences coordinate multiple devices to achieve specific energy management objectives. Phase 3 defines standard patterns for common scenarios.

Occupancy-Based HVAC Control

{
  "sequence_id": "seq-occ-control-001",
  "sequence_type": "occupancy_based_hvac_control",
  "building_id": "BLDG-001",
  "zone": "Conference-Room-A",
  "configuration": {
    "occupancy_sensor": "OCC-301-A",
    "controlled_equipment": ["AHU-301", "VAV-301-A"],
    "delays": {
      "occupied_to_vacant_minutes": 15,
      "vacant_to_occupied_minutes": 0
    },
    "setpoints": {
      "occupied": {
        "temperature_c": 22.0,
        "airflow_cfm": 1200,
        "lighting_percent": 100
      },
      "vacant": {
        "temperature_c": 26.0,
        "airflow_cfm": 400,
        "lighting_percent": 0
      }
    }
  },
  "performance_tracking": {
    "energy_baseline_kwh_per_day": 45.0,
    "target_savings_percent": 30,
    "comfort_constraints": {
      "max_recovery_time_minutes": 30,
      "min_acceptable_temp_c": 21.0,
      "max_acceptable_temp_c": 24.0
    }
  }
}

Demand Response Control

When grid demand response events occur, buildings must quickly reduce load while maintaining acceptable comfort levels.

{
  "dr_event_id": "dr-event-67890",
  "event_type": "peak_demand_reduction",
  "notification_time": "2025-01-15T13:00:00Z",
  "event_start": "2025-01-15T15:00:00Z",
  "event_end": "2025-01-15T18:00:00Z",
  "reduction_target_kw": 50,
  "actions": [
    {
      "priority": 1,
      "action": "adjust_hvac_setpoints",
      "equipment": ["AHU-301", "AHU-302"],
      "parameters": {
        "cooling_setpoint_adjustment_c": +2.0,
        "heating_setpoint_adjustment_c": -2.0
      },
      "projected_reduction_kw": 20,
      "comfort_impact": "low"
    },
    {
      "priority": 2,
      "action": "reduce_lighting",
      "zones": ["Open-Office-East", "Open-Office-West"],
      "parameters": {
        "brightness_reduction_percent": 25
      },
      "projected_reduction_kw": 15,
      "comfort_impact": "low"
    },
    {
      "priority": 3,
      "action": "defer_equipment",
      "equipment": ["DHW-PUMP-1"],
      "parameters": {
        "defer_until": "2025-01-15T18:30:00Z"
      },
      "projected_reduction_kw": 10,
      "comfort_impact": "none"
    },
    {
      "priority": 4,
      "action": "battery_discharge",
      "equipment": ["BESS-001"],
      "parameters": {
        "discharge_kw": 25
      },
      "projected_reduction_kw": 25,
      "comfort_impact": "none"
    }
  ]
}

Predictive Maintenance Protocol

Predictive maintenance protocols monitor equipment performance, detect degradation, and schedule maintenance before failures occur.

Condition Monitoring

Continuous monitoring of equipment parameters enables early detection of performance degradation.

Equipment Type	Monitored Parameters	Degradation Indicators	Typical Lead Time
Chillers	Efficiency, refrigerant pressure, vibration	Declining COP, pressure anomalies	2-4 weeks
Air Handlers	Fan power, filter ΔP, airflow	Increasing power, rising ΔP	1-2 weeks
Boilers	Combustion efficiency, stack temp	Declining efficiency, high stack temp	2-6 weeks
Pumps	Power, flow, vibration	Increasing power for same flow	2-4 weeks
Battery Storage	Capacity, internal resistance	Declining capacity, rising resistance	4-12 weeks

{
  "maintenance_alert_id": "maint-alert-456",
  "alert_type": "predictive_maintenance",
  "severity": "medium",
  "equipment_id": "AHU-301",
  "equipment_type": "air_handling_unit",
  "issue_detected": "filter_pressure_drop_high",
  "detection_time": "2025-01-15T14:30:00Z",
  "analysis": {
    "current_filter_dp_pa": 225,
    "normal_filter_dp_pa": 100,
    "max_filter_dp_pa": 250,
    "time_to_max_estimated_days": 7,
    "trend": "increasing",
    "confidence": 0.92
  },
  "recommendation": {
    "action": "replace_air_filters",
    "urgency": "schedule_within_week",
    "parts_needed": ["FILTER-20x25x4-MERV13"],
    "estimated_duration_hours": 2,
    "projected_cost_usd": 450
  },
  "impact_if_deferred": {
    "excess_energy_cost_per_day": 15.50,
    "failure_probability_increase": 0.15,
    "comfort_impact": "reduced_airflow"
  }
}

Maintenance Workflow

Fault Detection and Diagnostics (FDD)

FDD protocols automatically identify equipment faults, diagnose root causes, and recommend corrective actions.

Common Fault Patterns

{
  "fdd_alert_id": "fdd-alert-789",
  "fault_type": "simultaneous_heating_cooling",
  "severity": "high",
  "detection_time": "2025-01-15T14:30:00Z",
  "location": {
    "building_id": "BLDG-001",
    "floor": 3,
    "zone": "East-Wing"
  },
  "evidence": {
    "heating_valve_position_percent": 45,
    "cooling_valve_position_percent": 30,
    "zone_temperature_c": 22.5,
    "temperature_setpoint_c": 22.0,
    "outdoor_temperature_c": 15.0
  },
  "diagnosis": {
    "root_cause": "control_logic_error",
    "probable_causes": [
      {
        "cause": "Misconfigured deadband",
        "probability": 0.65,
        "test": "Check deadband setting in controller"
      },
      {
        "cause": "Faulty temperature sensor",
        "probability": 0.25,
        "test": "Verify sensor calibration"
      },
      {
        "cause": "Stuck heating valve",
        "probability": 0.10,
        "test": "Manually cycle valve"
      }
    ]
  },
  "impact": {
    "energy_waste_kwh_per_day": 85,
    "cost_waste_usd_per_day": 12.75,
    "duration_days": 3,
    "total_wasted_usd": 38.25
  },
  "recommendation": {
    "immediate_action": "Set deadband to 1.0°C",
    "verification": "Monitor valve positions for 24 hours",
    "follow_up": "Sensor calibration if issue persists"
  }
}

Fault Type	Indicators	Typical Impact	Priority
Simultaneous Heating/Cooling	Both valves open simultaneously	30-50% energy waste	High
Night/Weekend Operation	Equipment running when unoccupied	20-40% energy waste	High
Stuck Damper/Valve	Position doesn't change despite commands	10-30% energy waste	Medium
Sensor Drift	Readings inconsistent with other sensors	5-15% energy waste	Medium
Excessive Runtime	Equipment runs longer than required	15-25% energy waste	Medium
Short Cycling	Equipment cycles on/off too frequently	10-20% energy waste, equipment wear	High

Energy Optimization Protocols

Optimization protocols coordinate building systems to minimize energy consumption while maintaining comfort and operational requirements.

Model Predictive Control (MPC)

MPC uses building models and weather forecasts to proactively optimize energy use.

{
  "optimization_id": "opt-mpc-123",
  "optimization_type": "model_predictive_control",
  "building_id": "BLDG-001",
  "planning_horizon_hours": 24,
  "optimization_interval_minutes": 15,
  "objective": "minimize_cost",
  "inputs": {
    "weather_forecast": {
      "source": "NOAA",
      "resolution_hours": 1,
      "parameters": ["temperature", "solar_radiation", "wind_speed"]
    },
    "occupancy_schedule": {
      "monday_friday": [
        {"time": "07:00", "occupancy": 0.1},
        {"time": "09:00", "occupancy": 0.9},
        {"time": "12:00", "occupancy": 0.6},
        {"time": "17:00", "occupancy": 0.2},
        {"time": "19:00", "occupancy": 0.0}
      ]
    },
    "energy_prices": {
      "off_peak": 0.08,
      "mid_peak": 0.15,
      "on_peak": 0.25,
      "currency": "USD_per_kWh"
    }
  },
  "constraints": {
    "temperature_bounds_c": {"min": 21.0, "max": 24.0},
    "precool_allowed": true,
    "equipment_limits": {
      "max_startups_per_hour": 2,
      "min_runtime_minutes": 15
    }
  },
  "optimization_result": {
    "predicted_cost_usd": 245.50,
    "baseline_cost_usd": 325.75,
    "savings_usd": 80.25,
    "savings_percent": 24.6,
    "peak_demand_reduction_kw": 35.5
  }
}

Load Shifting Strategies

Load shifting moves energy consumption from high-cost to low-cost periods.

Strategy	Description	Typical Savings	Requirements
Thermal Precooling	Cool building before peak periods	15-30% peak reduction	Good building thermal mass
Ice/Chilled Water Storage	Make ice off-peak, use for cooling on-peak	40-60% peak reduction	Thermal storage tanks
Battery Arbitrage	Charge off-peak, discharge on-peak	30-50% peak reduction	Battery storage system
Equipment Scheduling	Run non-critical loads off-peak	10-20% cost reduction	Flexible operations

Protocol Integration and Coordination

Multiple protocols often operate simultaneously. Phase 3 defines how they coordinate without conflicting.

Priority Hierarchy

1. Life Safety: Fire, security, critical systems (highest priority)
2. Equipment Protection: Prevent damage to expensive equipment
3. Occupant Comfort: Maintain acceptable environmental conditions
4. Demand Response: Grid events and emergency load reduction
5. Energy Optimization: Cost and consumption minimization
6. Routine Operations: Standard scheduling and control

Conflict Resolution

{
  "conflict_id": "conflict-456",
  "timestamp": "2025-01-15T14:30:00Z",
  "conflicts": [
    {
      "protocol_1": "demand_response",
      "action_1": "increase_temperature_setpoint",
      "new_setpoint_c": 26.0
    },
    {
      "protocol_2": "occupant_comfort",
      "action_2": "maintain_temperature_setpoint",
      "current_setpoint_c": 22.0
    }
  ],
  "resolution": {
    "winning_protocol": "demand_response",
    "rationale": "DR event has higher priority than optimization",
    "compromise": {
      "setpoint_c": 24.0,
      "duration_minutes": 60,
      "comfort_override_available": true
    }
  }
}

Implementation Best Practices

Successfully implementing Phase 3 protocols requires careful design and testing.

Testing and Validation

• Simulation first: Test protocols in digital twins before deployment
• Progressive rollout: Start with non-critical systems and zones
• Monitor closely: Track all protocol actions and impacts during initial deployment
• Maintain overrides: Always provide manual override capability
• Log comprehensively: Record all protocol decisions for analysis
• Performance tracking: Measure actual vs. predicted savings

Reliability and Resilience

• Graceful degradation: Protocols should fail safely if data unavailable
• Timeout handling: Commands must timeout if not acknowledged
• Retry logic: Implement exponential backoff for transient failures
• Health monitoring: Protocols self-monitor and report status
• Fallback modes: Revert to safe defaults if protocol fails