buildyn-thermal-dynamics-control

name: buildyn-thermal-dynamics-control description: "Excitation-driven data generation framework for building thermal dynamics modeling and control. BuilDyn enables customizable excitation strategies for control-oriented data generation, supports sampling from representative building distributions, and provides Python interface for ML pipelines. Use when: (1) training data-driven building models, (2) ensuring sufficient state-space exploration, (3) preparing training data for building control applications, (4) generating excited vs stationary operation datasets, (5) developing building-specific foundation models." license: Complete terms in LICENSE.txt metadata: arxiv_id: "2605.29849" published: "2026-05-28" authors: "Felix Koch, Thomas Krug, Fabian Raisch, Benjamin Schäfer, Benjamin Tischler" tags: [building-thermal, excitation-driven, control-oriented, machine-learning, BuilDyn, foundation-models, systems-control]

BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics

Framework for generating control-oriented training data through systematic excitation of building thermal systems.

Problem Context

Challenge: Existing building datasets and simulations reflect stationary operation under fixed control policies, leading to:

• Limited exploration of control-driven state space
• Poor generalization to unseen operating conditions
• Reduced robustness in downstream ML tasks (fault detection, energy-efficient control)

Solution: BuilDyn enables excitation-driven data generation to ensure sufficient state-space coverage.

Core Concepts

1. Excitation Strategies

Goal: Explore control-driven system state space systematically.

Types of excitation:

• Random excitation: Stochastic control inputs
• Periodic excitation: Sinusoidal or square-wave signals
• Step excitation: Sudden control changes
• Optimized excitation: Design signals to maximize information gain

Key parameters:

• Excitation amplitude (Δu)
• Excitation frequency (f)
• Excitation duration (T)
• Coverage metrics (state-space volume)

2. Building Sampling

BuilDyn supports sampling from representative building distributions:

# Sample buildings from distribution
building_params = {
    'thermal_mass': sample_thermal_mass(),
    'window_area': sample_window_area(),
    'insulation': sample_insulation(),
    'occupancy_patterns': sample_occupancy(),
    'climate_zone': sample_climate()
}

This enables:

• Training on diverse building characteristics
• Transfer learning across building types
• Foundation model development

3. Python Interface

Integration into ML pipelines:

from buildyn import BuilDynGenerator

# Initialize generator
generator = BuilDynGenerator(
    building_distribution='representative_buildings.json',
    excitation_strategy='optimized',
    duration_hours=168
)

# Generate excited dataset
dataset = generator.generate(
    n_buildings=100,
    n_scenarios_per_building=10
)

# Export for ML training
dataset.save('excited_building_data.h5')

Implementation Workflow

Step 1: Define Excitation Strategy

excitation_config = {
    'type': 'optimized',  # 'random', 'periodic', 'step', 'optimized'
    'control_variables': ['heating_power', 'cooling_power', 'ventilation_rate'],
    'amplitude_range': [0.0, 1.0],  # Normalized control range
    'duration': 168,  # Hours (1 week)
    'sampling_rate': 3600  # Seconds (1 hour intervals)
}

Step 2: Sample Building Parameters

building_distribution = {
    'thermal_mass': {
        'distribution': 'uniform',
        'range': [1e6, 5e7]  # J/K
    },
    'window_area_fraction': {
        'distribution': 'normal',
        'mean': 0.15,
        'std': 0.05
    },
    'insulation_R': {
        'distribution': 'lognormal',
        'mean': 2.5,  # m²·K/W
        'std': 0.5
    }
}

Step 3: Generate Training Data

# Generate excited data
excited_data = generator.generate_excited(
    n_samples=10000,
    excitation_config=excitation_config,
    building_distribution=building_distribution
)

# Generate baseline (non-excited) data for comparison
baseline_data = generator.generate_stationary(
    n_samples=10000,
    control_policy='fixed_setpoint',
    building_distribution=building_distribution
)

Step 4: Train ML Models

# Compare performance
model_excited = train_model(excited_data)
model_baseline = train_model(baseline_data)

# Evaluate on unseen operating conditions
test_scenarios = generate_challenging_scenarios()
performance_excited = evaluate(model_excited, test_scenarios)
performance_baseline = evaluate(model_baseline, test_scenarios)

# Expected: excited model > baseline model on unseen conditions

Key Advantages

1. State-space coverage: Systematic exploration of control inputs
2. Robustness: Better generalization to unseen operating conditions
3. Diversity: Sampling from representative building distributions
4. Integration: Python interface for easy ML pipeline integration
5. Foundation models: Enables building-specific foundation model development

Excitation Design Principles

Maximizing Information Gain

Excitation signals should:

• Cover wide range of control inputs (heating, cooling, ventilation)
• Explore different thermal dynamics regimes
• Avoid redundant trajectories
• Maximize Fisher information or mutual information

Coverage Metrics

Quantify state-space coverage:

def compute_coverage(dataset):
    state_vectors = dataset['states']  # [temperature, humidity, CO2, etc.]
    
    # Volume coverage
    volume = convex_hull_volume(state_vectors)
    
    # Density coverage
    entropy = state_entropy(state_vectors)
    
    return {'volume': volume, 'entropy': entropy}

Use Cases

• Fault detection & diagnosis: Training robust FDD models
• Energy-efficient control: Model predictive control (MPC) training data
• Building energy simulation: Validating simulation models
• Transfer learning: Pre-training foundation models for specific buildings
• Climate adaptation: Training models for diverse weather conditions

Comparison: Excited vs Stationary Data

Practical Considerations

• Excitation duration: Balance exploration vs stability (1-2 weeks typical)
• Sampling rate: Match downstream model requirements (hourly for thermal dynamics)
• Building diversity: Sample from realistic distributions
• Climate variation: Include diverse weather scenarios
• Occupancy patterns: Account for usage variability

Integration with Building Simulation

BuilDyn builds on BuilDa (Building Data simulator):

# BuilDyn extends BuilDa
from builda import BuildingSimulator
from buildyn import ExcitationModule

simulator = BuildingSimulator(building_config)
exciter = ExcitationModule(strategy='optimized')

# Excited simulation
control_sequence = exciter.generate_controls(duration=168)
simulation_results = simulator.run(control_sequence)

Research Directions

1. Foundation models: Building-specific foundation models via large-scale excited data
2. Transfer learning: Cross-building knowledge transfer
3. Adaptive excitation: Online excitation based on model uncertainty
4. Multi-objective excitation: Balance energy cost vs information gain

Activation Keywords

• building thermal dynamics
• excitation-driven data
• control-oriented modeling
• building ML training data
• BuilDyn framework
• building foundation models
• thermal system excitation