Multi-Objective Energy & Comfort Optimization for a Helsinki Residential Building
A multi-objective optimization study for a multi-story residential building in Helsinki, Finland — using DesignBuilder/EnergyPlus simulation and a genetic-algorithm search to find the best trade-off between annual energy consumption and occupant thermal comfort.
Challenge
Helsinki's cold climate drives high annual heating demand, and the project needed a data-driven way to balance two competing goals — cutting total site energy consumption while keeping occupants thermally comfortable — rather than relying on a single fixed setpoint schedule chosen by guesswork.
Approach
GreenBIMSource built a detailed DesignBuilder model of the residential building and ran a full-year EnergyPlus simulation of heating, cooling, and internal gains. A multi-objective genetic-algorithm optimization was then run across cooling/heating setpoint and setback temperatures and occupancy schedules — evaluating dozens of generations to trace out the Pareto front between total site energy consumption and occupant discomfort hours.
Result
The optimization produced a set of Pareto-optimal setpoint schedules rather than one guessed answer: solutions ranged from roughly 1.9 GWh of annual site energy with about 1,770 discomfort-hours at one end, down to about 2.35 GWh with discomfort reduced to roughly 1,050 hours at the other. That gave the design team a quantified trade-off curve — smart scheduling of setpoints alone was enough to meaningfully shift the balance between energy use and comfort, without changing the building envelope.
Deliverables
- DesignBuilder model + full-year EnergyPlus simulation (heating, cooling, internal gains)
- Multi-objective genetic-algorithm optimization of setpoint schedules
- Pareto front of energy consumption vs. occupant discomfort
- Data-driven setpoint schedule recommendation
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