# HOT Dataset: HVAC Operations Transfer


## Overview

The **HOT (HVAC Operations Transfer)** dataset is the first large-scale open-source dataset purpose-built for transfer learning research in building control systems. Buildings account for approximately 10-15% of global energy consumption through HVAC systems, making intelligent control optimization critical for energy efficiency and climate change mitigation.

### Key Statistics
- **159,744** unique building-weather combinations
- **15,808** building models with controllable zone-level setpoints
- **19** ASHRAE climate zones across **76** global locations
- **16** commercial building types (office, retail, school, hospital, etc.)
- **12** systematic occupancy patterns
- **3** thermal performance scenarios
- **15-minute** timestep resolution for control applications

## Dataset Description

HOT addresses the critical infrastructure gap in building control transfer learning by providing systematic variations across four key context dimensions:

### 🏢 **Building Geometry** (16 Types)
- **Office**: Small (511 m²), Medium (4,983 m²), Large (46,323 m²)
- **Retail**: Standalone (2,294 m²), Strip Mall (2,294 m²)
- **Educational**: Primary School (6,874 m²), Secondary School (19,592 m²)
- **Healthcare**: Hospital (22,443 m²), Outpatient (3,805 m²)
- **Hospitality**: Small Hotel (3,726 m²), Large Hotel (11,349 m²)
- **Residential**: Midrise Apartment (2,825 m²), Highrise Apartment (7,063 m²)
- **Food Service**: Sit-down Restaurant (511 m²), Fast Food (232 m²)
- **Industrial**: Warehouse (4,835 m²)

### 🌡️ **Thermal Performance** (3 Scenarios)
- **Default**: Baseline thermal properties (R_mult = 1.0)
- **High Performance**: Enhanced insulation (R_mult = 2.0, U_mult = 0.5)
- **Low Performance**: Minimal insulation (R_mult = 0.5, U_mult = 2.0)

### 🌍 **Climate Conditions** (76 Locations)
- **Complete ASHRAE coverage**: All 19 climate zones (0A through 8)
- **Global diversity**: From tropical (Ho Chi Minh) to subarctic (Fairbanks)
- **Weather data types**: TMY (Typical Meteorological Year) + Real historical (2014-2024)
- **Temperature range**: -44.4°C to 47.0°C
- **HDD range**: 0 to 7,673 heating degree days
- **CDD range**: 6 to 4,301 cooling degree days

### 👥 **Occupancy Patterns** (12 Schedules)
- **Standard**: Traditional office hours (8 AM - 5 PM weekdays)
- **Low/High Occupancy**: 50%/150% intensity variations
- **Shift Operations**: Early (6 AM-3 PM), Late (2 PM-11 PM)
- **Sector-Specific**: Retail (10 AM-9 PM), School (7 AM-4 PM + evening)
- **Healthcare**: Hospital 24/7 with shift patterns
- **Modern Work**: Flexible hybrid with staggered hours
- **Specialized**: Gym (morning/evening peaks), Warehouse logistics
- **Continuous**: 24/7 operations

## Dataset Structure
HOT/
├── data/
│   ├── base/                          # Raw building models by geometry type
│   │   ├── ApartmentHighRise_STD2013/
│   │   ├── ApartmentMidRise_STD2013/
│   │   ├── Hospital_STD2013/
│   │   ├── OfficeSmall_STD2013/
│   │   └── ...                        # 16 building geometry folders
│   ├── processed/
│   │   └── base/                      # Processed EPJSONs ready for control
│   │       ├── ApartmentHighRise_STD2013.epJSON
│   │       ├── Hospital_STD2013.epJSON
│   │       └── ...                    # All processed buildings
│   ├── variations/                    # Building variations
│   │   ├── occupancy/                 # Occupancy schedule variations
│   │   │   ├── standard/
│   │   │   ├── low_occupancy/
│   │   │   ├── hospital/
│   │   │   └── ...                    # 12 occupancy patterns
│   │   ├── thermal/                   # Thermal performance variations
│   │   │   ├── default/
│   │   │   ├── high_performance/
│   │   │   └── low_performance/
│   │   └── combined/                  # Multi-variable combinations
│   │       ├── occupancy_24_7_thermal_default/
│   │       ├── occupancy_hospital_thermal_high_performance/
│   │       └── ...                    # All combinations
│   ├── weather/                       # Weather data files (.epw)
│   │   ├── base/                      # Base TMY weather files (19 locations)
│   │   ├── expanded/                  # Extended TMY files (57 additional locations)
│   │   ├── real_base/                 # Historical weather (2014-2024)
│   │   └── tables/                    # Weather metadata tables
│   └── tables/                        # Dataset metadata and combinations
│       ├── buildings.csv              # Building characteristics
│       └── building_weather_combinations.csv  # All 159,744 pairings

## Key Features

### 🎮 **Reinforcement Learning Ready**
- **Controllable setpoints**: Zone-level heating/cooling temperature control
- **Gymnasium interface**: Standard RL environment wrapper
- **Comprehensive state space**: Zone temperatures, outdoor conditions, energy consumption
- **Multi-objective rewards**: Energy efficiency + thermal comfort + control stability
- **EnergyPlus integration**: Physics-based building simulation

### 🔬 **Transfer Learning Framework**
- **Similarity metrics**: Quantitative compatibility assessment across 4 dimensions
- **Zero-shot evaluation**: Direct policy transfer without retraining
- **Systematic variations**: Single and multi-variable transfer scenarios
- **Benchmark protocols**: Standardized evaluation methodology

### 🌐 **Global Climate Coverage**
- **All inhabited regions**: Complete ASHRAE climate zone representation
- **Real vs. synthetic**: TMY baseline + historical weather variability
- **Extreme conditions**: From subarctic (-44°C) to desert (+47°C)
- **Transfer analysis**: Climate adaptation and geographic deployment

### 📊 **Research Infrastructure**
- **Standardized formats**: Consistent EnergyPlus epJSON structure
- **Processing pipeline**: Automated building modification tools
- **Validation tools**: Building model verification and testing
- **Similarity analysis**: Transfer feasibility assessment toolkit

## Research Applications

### 🤖 **Reinforcement Learning**
- **Multi-agent control**: Coordinate multiple HVAC zones
- **Meta-learning**: Fast adaptation to new buildings (MAML, Reptile)
- **Foundation models**: Pre-train on diverse building types
- **Safe RL**: Constraint-aware control with comfort guarantees

### 🔄 **Transfer Learning**
- **Domain adaptation**: Geographic and climate transfer
- **Few-shot learning**: Minimal data adaptation for new buildings
- **Cross-building generalization**: Policy transfer across archetypes
- **Similarity-guided selection**: Optimal source building identification

### 📈 **Building Analytics**
- **Energy benchmarking**: Performance comparison across climates
- **Occupancy analysis**: Usage pattern impact on energy consumption
- **Envelope optimization**: Thermal performance sensitivity analysis
- **Climate resilience**: Building adaptation to changing conditions

## Dataset Statistics

| **Dimension** | **Count** | **Range** | **Examples** |
|---------------|-----------|-----------|--------------|
| Building Types | 16 | 232-46,323 m² | Office, Hospital, School |
| Climate Zones | 19 | -44°C to +47°C | 0A (Tropical) to 8 (Subarctic) |
| Occupancy Schedules | 12 | 53-168 hrs/week | Office, Retail, Hospital, 24/7 |
| Thermal Scenarios | 3 | 0.5-2.0× resistance | High/Default/Low performance |
| Weather Files | 192 | TMY + Real (2014-2024) | Geographic + temporal variation |

## File Formats

### Building Models (`.epJSON`)
- **Format**: EnergyPlus JSON input files
- **Features**: Zone-level controllable setpoints, comprehensive meters
- **Compatibility**: EnergyPlus 24.1+
- **Size**: ~50-500 KB per building

### Weather Files (`.epw`)
- **Format**: EnergyPlus Weather format
- **Frequency**: Hourly meteorological data
- **Variables**: Temperature, humidity, solar, wind
- **Size**: ~1-2 MB per location-year

### Metadata Tables (`.csv`)
- **Buildings**: Physical characteristics, variations, file paths
- **Weather**: Climate statistics, location data, file paths
- **Combinations**: Valid building-weather pairings (159,744 total)

## Benchmarks and Baselines

### Control Algorithms
- **Static Baseline**: Seasonal ASHRAE setpoint schedules
- **PPO**: Proximal Policy Optimization with building-specific tuning

### Transfer Learning Methods
- **Zero-shot**: Direct policy application without retraining
- **Fine-tuning**: Limited adaptation with target building data
- **Meta-learning**: MAML and Reptile for fast adaptation

### Evaluation Metrics
- **Transfer Performance Ratio**: Transferred vs. target-trained performance
- **Energy Efficiency**: HVAC consumption reduction vs. baseline
- **Comfort Violations**: Hours outside desired temperature range
- **Training Acceleration**: Reduced learning time through transfer

## Citation

If you use the HOT dataset in your research, please cite:

```bibtex
@inproceedings{2025hot,
  title={A HOT Dataset: 150,000 Buildings for HVAC Operations Transfer Research},
  author={anonymous},
  booktitle={x},
  year={2025},
  publisher={x}
}
```

## License

This dataset is released under the MIT License. See `LICENSE` file for details.

## Contributing

We welcome contributions to expand and improve the HOT dataset:

- **New building types**: Additional commercial/residential archetypes
- **Climate expansion**: More geographic locations and weather data
- **Enhanced metadata**: Additional building characteristics
- **Analysis tools**: Transfer learning evaluation scripts
- **Bug reports**: Issues with building models or processing

## Support and Contact

- **Issues**: [GitHub Issues](https://github.com/your-org/building-generator/issues)
- **Discussions**: [Hugging Face Discussions](https://huggingface.co/datasets/BuildingBench/HOT/discussions)
- **Email**: anonymous for now


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**HOT Dataset** - Advancing building energy research through comprehensive, standardized, and globally-representative data for intelligent HVAC control systems.