HAEO vs EMHASS¶

When exploring energy optimization solutions for Home Assistant, you'll likely encounter two actively maintained projects: HAEO and EMHASS. Both aim to optimize home energy usage but take fundamentally different architectural approaches. This page provides a fair, technical comparison to help you choose the solution that best fits your needs.

Quick comparison¶

Philosophical differences¶

The core difference between HAEO and EMHASS reflects their origins and design philosophies:

EMHASS (developed by an electrical engineer) takes an integrated approach: it's purpose-built for energy management with forecasting, machine learning, and discrete load scheduling all included. It assumes a more standard system topology and provides comprehensive features in a single package.

HAEO (developed by a software engineer) follows the Unix philosophy: do one thing well. It focuses purely on optimization with a flexible, graph-based network model, relying on other Home Assistant integrations to provide forecasting data. The flexibility to model any network topology through connections is its defining characteristic.

You might think of it as: an electrical engineer building a software project versus a software engineer building an electrical project.

EMHASS¶

GitHub • Documentation • Community discussion

Status: Actively maintained, mature project with established community

Overview¶

EMHASS (Energy Management for Home Assistant) is a Python-based add-on that optimizes home energy management through day-ahead optimization. It excels at scheduling deferrable loads (washing machines, dishwashers, EV chargers, pool pumps) to minimize costs and maximize self-consumption of solar energy.

Strengths¶

• Mixed integer linear programming: Can handle discrete decisions (on/off appliances), enabling true appliance scheduling optimization
• Built-in forecasting: Includes machine learning-based load forecasting and integrates with solar forecasting services (Solcast, Forecast.Solar)
• Purpose-built for deferrable loads: Designed specifically for appliance scheduling and load management
• Mature and proven: Established project with large community, extensive real-world deployments, and comprehensive documentation
• Separate machine capability: Can run on a different machine than Home Assistant, beneficial for resource-constrained systems
• Simple installation: Direct installation from Home Assistant add-on store
• Thermal load support: Can model and optimize thermal loads (hot water heaters, etc.)

Limitations¶

• Requires HA OS/Supervised: Add-on limitation means it won't work with Container or Core installations
• Fixed network topology: Less flexible for modeling custom or complex system architectures
• Configuration complexity: Despite simpler architecture, configuration can be complex and requires understanding many parameters
• Limited multi-element support: Harder to model multiple batteries, arrays, or custom grid configurations
• Integration overhead: Uses combination of sensors, REST API, and shell commands rather than native integration

Best for¶

• Users needing discrete appliance/load scheduling
• Those wanting built-in ML and solar forecasting
• Home Assistant OS or Supervised installations
• Standard solar + battery + grid setups
• Resource-constrained HA instances (can offload to separate machine)
• Users preferring add-on installation model
• Systems with thermal loads

HAEO¶

GitHub • Documentation • GitHub discussions

Status: Actively maintained, newer project

Overview¶

HAEO (Home Assistant Energy Optimizer) is a native Home Assistant integration that optimizes energy networks through flexible topology modeling. Its key innovation is the ability to model any network structure through connections between elements, enabling custom system configurations that emerge from the graph structure itself.

Strengths¶

• Flexible network topology: Model any system structure through connections - the strongest differentiator. Graph-based approach enables emergent behavior for complex systems
• Native Home Assistant integration: Works with any HA installation method (OS, Supervised, Container, Core)
• Full UI configuration: Everything configurable through Home Assistant's UI with organized devices
• Multiple element support: Easy support for multiple batteries, solar arrays, grids, and loads
• Modern codebase: Python 3.13+, platinum-level code quality standards, strong typing, comprehensive testing
• Lower latency: Runs alongside Home Assistant instance for minimal delay
• Native sensor integration: Sensors organized into devices, persist between reboots, leverage native HA features
• Unique features: Battery overcharge/undercharge pricing (economic incentives for extended SOC ranges), flexible network modeling via connections
• Extensibility: Graph structure allows modeling diverse energy systems without code changes

Limitations¶

• Continuous optimization: Linear programming only (MILP intentionally avoided for simplicity and solve speed), cannot optimize discrete appliance on/off decisions
• Graph complexity: Requires understanding topology and connection concepts, which adds initial learning curve
• Newer project: Smaller community, less proven track record compared to EMHASS
• Requires HACS: Additional step before installation (though HACS is very common)
• Setup complexity: Flexibility means more configuration options and decisions
• External forecasting dependency: Relies entirely on other HA integrations for forecast data
• Missing features: Thermal loads and deferrable load scheduling are planned future additions

Tradeoffs: HAEO trades appliance scheduling capability for simpler configuration, faster solve times, and more flexible network topology modeling. The linear programming approach ensures reliable sub-second optimization even on resource-constrained hardware.

Best for¶

• Complex or custom system topologies
• Users with multiple batteries, arrays, or grids
• Home Assistant Container or Core installations
• Those preferring native HA integration
• Users valuing UI-based configuration
• Systems needing modeling flexibility (AC/DC splits, hybrid inverters, multiple meters)
• Users who prioritize modern code quality and software architecture
• Systems requiring flexible battery SOC pricing strategies (overcharge/undercharge economics)

Technical comparison¶

Network modeling¶

Optimization¶

Integration and setup¶

Features¶

When to choose each solution¶

Choose EMHASS if you¶

• Need discrete appliance or load scheduling (washing machine, EV charger timing)
• Want built-in machine learning and solar forecasting without installing separate integrations
• Prefer add-on installation model
• Need to run optimization on a separate machine (resource-constrained HA)
• Want an established project with proven track record and large community
• Need thermal load optimization
• Are running Home Assistant OS or Supervised

Choose HAEO if you¶

• Have a complex or custom system topology that doesn't fit standard patterns
• Need to model multiple batteries, solar arrays, or grid connections
• Are running Home Assistant Container or Core (where add-ons aren't available)
• Prefer native Home Assistant integration with lower latency
• Want UI-based configuration for all settings
• Value modern codebase with strong typing and comprehensive testing
• Need battery overcharge/undercharge economic modeling
• Want to model non-standard systems (AC/DC splits, multiple meters, custom connections)
• Prioritize software quality and maintainability

Can you use both?¶

Technically, yes. They have overlapping capabilities but could be complementary:

• HAEO for battery and solar optimization with flexible topology
• EMHASS for discrete appliance scheduling

However, in practice, most users will choose one or the other since both handle battery and solar optimization, which creates redundancy. The overlap is significant enough that running both adds complexity without major benefit for most systems.

Making your choice¶

Consider these factors:

1. System complexity: Simple standard setup → either works; complex topology → HAEO
2. Installation method: HA OS/Supervised → either works; Container/Core → HAEO only
3. Optimization type: Appliance scheduling → EMHASS; battery/solar only → either works
4. Configuration preference: UI-based → HAEO; file-based acceptable → EMHASS
5. Forecasting: Want built-in → EMHASS; happy using other integrations → HAEO
6. Project maturity: Want established → EMHASS; modern codebase → HAEO
7. Resource constraints: Need separate machine → EMHASS; prefer integrated → HAEO

Getting help¶

HAEO support¶

• GitHub issues - Bug reports and feature requests
• GitHub discussions - Questions and community support
• Documentation - Comprehensive guides

EMHASS support¶

• GitHub repository - Code and issues
• Documentation - Setup and configuration guides
• Community forum - Active discussion thread

Conclusion¶

Both HAEO and EMHASS are actively maintained, quality projects that solve real energy optimization problems for Home Assistant users. They represent different architectural philosophies:

• EMHASS: Integrated solution with rigid structure, built-in forecasting, and discrete optimization capabilities
• HAEO: Modular solution with flexible structure, external forecasting, and continuous optimization focus

Neither is objectively "better" - the right choice depends on your specific system, installation method, optimization needs, and preferences. EMHASS excels at appliance scheduling with its MILP solver and offers a comprehensive, proven solution. HAEO excels at flexible topology modeling and native integration with modern code quality.

Choose based on what matters most for your use case: discrete control and built-in forecasting (EMHASS), or flexible topology and native integration (HAEO).

Next steps¶