AI penalty
myCoreAI balances several competing goals on every optimization cycle — occupant comfort, equipment efficiency, and operating cost. Each undesirable outcome carries a penalty, and myCoreAI selects the candidate plan with the lowest total penalty over the optimization horizon.
This page describes how those priorities are ordered.
How the penalty system works
On every cycle, myCoreAI evaluates many candidate setpoint plans across a 72-hour horizon, predicts the outcome of each against the building model, sums the penalties, and selects the lowest-cost plan. Only the first hour is dispatched to the Building Management System (BMS); the remaining horizon ensures the current decision leads to a coherent trajectory.
Penalties are not equal in weight, and several are conditional on season, time of day, or occupancy.
Priority 1 — Comfort
Comfort penalties dominate. myCoreAI weights them far above any energy or cost term, so it accepts higher energy use rather than allow comfort to drift outside the comfort bounds.
Comfort penalties cover:
Indoor temperature outside the comfort bounds
CO₂ levels above the configured limit
Humidity above the configured limit, where measured
For buildings with sensor groupings configured, myCoreAI evaluates comfort per group (typically one group per heating or cooling circuit) rather than per individual sensor.
Priority 2 — Air handling unit behavior
For each air handling unit (AHU), myCoreAI chooses between heating, cooling, or ventilation-only based on outdoor temperature and season.
Winter
Heating via AHU
High — discouraged unless comfort requires it or Heat with ventilation is enabled
Summer
Free cooling via AHU
Low — encouraged before mechanical cooling
Year-round
Fan / pressure operation
Small constant — discourages unnecessary ventilation
Priority 3 — Heating source
myCoreAI assigns a low penalty to the primary heating circuits (typically district heating via radiators) and a high penalty to AHU-based heating. This ranking produces the winter logic above.
A smoothness term discourages rapid hour-to-hour changes in heating setpoints. The Max change heating circuit setting in AI settings caps how fast setpoints can move.
Priority 4 — Cooling source
Cooling sources carry light penalties, ordered by cost and environmental impact: free cooling via ventilation, then district cooling, then mechanical cooling.
Energy price modulation
When myLoadshift is active, myCoreAI multiplies energy-related penalties by the time-varying spot price and shifts consumption toward cheaper hours. Comfort penalties are not price-modulated.
Energy cap
When energy cap is configured, an additional penalty activates as the predicted daily average approaches the cap. myCoreAI weights it heavily — the cap behaves as a near-hard constraint.
Putting it together
myCoreAI scores each candidate plan on:
Comfort penalties — high, usually zero when comfort is maintained
Equipment-use penalties — moderate, season- and temperature-dependent
Smoothness penalties — small, keep operation stable
Cost-related penalties — small, favor cheaper energy when other constraints are satisfied
Cap penalties — active only near the energy cap
The lowest-total-cost plan is dispatched.
Worked example
A district-heated office on a mild February afternoon: outdoor temperature is 6 °C, indoor sensors sit mid-band, and the spot price drops sharply between 13:00 and 15:00. With myLoadshift active, myCoreAI lifts the radiator supply slightly during the cheap window to pre-charge the building mass, then lets it coast through the early evening price peak. Comfort penalties stay at zero throughout — the move is driven entirely by the cost term, bounded by the smoothness cap on the heating circuit.
Interpreting unexpected behavior
When setpoints look counterintuitive — radiator supply unchanged on a mild day, AHU running warmer than expected — a higher-priority penalty is typically active: a comfort sensor near its limit, the smoothness term preventing a fast drop, or the building approaching its energy cap.
To bias myCoreAI toward more aggressive energy savings, adjust AI settings: widen the adaptive min/max, enable AI Freedom, or increase off-hours offsets.
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