Our AI-based heat supply system delivers heat loss reduction through heat load forecasting and intelligent ITP control. By leveraging RL heat management and a hybrid thermal balance model, we achieve heat network optimization and fault detection in heat point automation. Using machine learning heat supply techniques, we forecast load with MAPE 3-6%. Over 5 years, we have deployed such solutions in 30+ projects — from single boiler houses to district networks. Average heat savings amount to 1–2 million rubles per heating season (e.g., 1.5 million rubles for a 10,000 Gcal/year network), and the system payback period does not exceed 2 years. The typical investment for our system is from 0.8 to 1.5 million rubles.
Why traditional control falls short
The classical temperature curve of centralized heat supply (CHS) is a fixed curve: the supply temperature depends only on the current outdoor temperature. This ignores the thermal inertia of buildings (1–6 hours depending on mass) and leads to overheating during warm spells or underheating during sudden cold snaps. Unlike classical control, our heat network optimization using heat load forecasting and a hybrid thermal balance model reduces heat loss.
Building heat balance — the basis of the physical model:
Q_loss = U_building × A × (T_indoor - T_outdoor) + Q_ventilation Q_needed = Q_loss - Q_solar_gain - Q_internal_gain The U-value (thermal conductivity) is determined from heat meter data and historical temperatures via regression. This allows the model to adapt to the actual characteristics of the building.
How heat load is forecasted
Input data:
heating_features = { # Weather (main driver) 'temp_outside': outdoor_temperature, 'temp_forecast_6h': temperature_6h_ahead, 'wind_speed': wind_speed, # convective losses 'solar_radiation': ghi, # passive solar heating # Building/network 'temp_indoor_setpoint': 22.0, 'building_heat_loss_coeff': U_building, 'thermal_mass': building_thermal_mass, # Historical 'heat_demand_lag_1h': heat_demand_1h_ago, 'heat_demand_lag_24h': heat_demand_24h_ago, # Context 'hour': hour_of_day, 'is_occupied': occupancy_schedule, # working hours vs. night 'day_type': encode(workday_weekend_holiday) } Models:
- RC-model (Resistance-Capacitance): physical heat balance model. Parameters identified from automated metering data (ASCUE).
- ML (LightGBM): better captures anomalies (wind in cracks, unexpected insulation failures).
- Hybrid: RC-model + ML residual correction.
| Method | MAPE (24h) | Implementation complexity | Application area |
|---|---|---|---|
| Simple regression | 10-15% | Low | Estimates not requiring high accuracy |
| RC-model | 5-8% | Medium | Typical buildings, stable network |
| Hybrid (RC+ML) | 3-6% | High | Complex networks, anomalies, optimization |
Accuracy: MAPE 3-6% for hourly forecast up to 24 hours.
Temperature curve optimization
Traditional CHS temperature curve — a fixed curve in the ITP controller. AI replaces it with dynamic calculation:
def optimal_supply_temperature(T_outdoor, T_indoor_target, Q_predicted, hydraulic_state, network_losses): """ Minimize: gas_consumption(T_supply) Constraint: T_indoor >= T_target for all consumers """ # Hydraulic network model → temperature at each consumer # as a function of T_supply and flow rates T_consumer = hydraulic_model(T_supply, flow_rates) constraint = T_consumer.min() >= T_indoor_target # Optimize result = minimize_gas(T_supply, constraints=[constraint]) return result.x Weather-based control with forecast:
- Classical: adjustment based on current outdoor T
- AI: adjustment based on outdoor T in 2-3 hours (accounting for building thermal inertia)
This prevents overheating during warm spells and underheating during sudden cold snaps.
What makes our approach unique
| Parameter | Traditional Control | AI Control |
|---|---|---|
| Temperature curve | Fixed, based on current T_outdoor | Dynamic, with 6-hour forecast |
| Thermal inertia consideration | No | Explicitly modeled (RC-model) |
| Building adaptation | Only via manual tuning | Automatic parameter identification |
| Anomaly response | Operator sees in 2–4 hours | ML detector in 15 minutes |
Automatic ITP control
ITP (Individual Thermal Point) — the regulation point for a building: Controlled parameters:
- Coolant supply temperature
- Flow rate (via control valve)
- DHW (domestic hot water) mode
SCADA/ACS:
- ITP controllers: Siemens PLC / Owen PLC
- Protocols: Modbus TCP, MQTT for IoT sensors
- SCADA: ZENON, IntegraTOOL
ML decision-making model for ITP: An RL agent controls the valve, receiving observation: T_indoor, T_supply, T_outdoor_forecast. Reward: -energy_consumed with T_indoor >= setpoint.
Leak and fault detection
Heat loss analysis: Compare: heat supplied by source vs. heat received by consumers. Difference = network losses. An anomalous increase in losses → possible pipeline failure.
def detect_network_leak(supply_heat, return_heat, consumer_receipts): theoretical_losses = supply_heat - consumer_receipts actual_losses = supply_heat - return_heat # from metering devices unexplained_loss = actual_losses - theoretical_losses if unexplained_loss / supply_heat > 0.05: # >5% sudden losses alert("Possible network failure, localize by section") Network segmentation: Hydraulic network model + anomaly detection → localize the leak section to within 200–500 m.
Integration with GIS: QGIS / ArcGIS + pipeline database → visualize anomalies on a map → operator sees the exact section.
How we do it: the process
- Analytics (1–2 weeks): collect heat meter data, weather archives, network diagrams. Identify key nodes.
- Design (1–2 weeks): create a physical network model, choose ML architecture (LightGBM + RC). Set up MLOps pipeline.
- Implementation (3–4 weeks): develop forecasting models, temperature curve optimizer, RL agent. Integrate with SCADA.
- Test (1 week): run in shadow mode (model advises but does not control). Compare with real data.
- Deployment (1 week): launch into production, train operators.
Deliverables
- Documentation: system architecture, data model, API specification.
- Access to servers with models and Grafana dashboards (all metrics in real time).
- Training: workshop for operators and engineers (4 hours).
- Support: 3 months of post-production monitoring and model fine-tuning.
System metrics
- Gas savings: 8-15% with AI control vs. fixed curve
- Complaints about overheating/underheating: 50-70% reduction
- Heat load forecast MAPE: <5% (2-3 times better than traditional regression with 10-15%)
- Fault localization time: from 4-8 hours down to 30-60 minutes
Why choose us
- 5 years in AI optimization, 30+ successful projects in heat supply.
- Certified SCADA and ML engineers (Siemens, PyTorch).
- We guarantee 8–15% savings — if not achieved, we refine for free.
In summary, our approach combines AI heat supply, machine learning heat supply, and RL heat management for comprehensive heat network optimization and fault detection.
According to thermal comfort, maintaining temperature within ±1°C is critical for buildings. Our system keeps it within these limits while saving resources.
More about the RC-model
The RC-model represents the building as an electrical circuit: thermal resistance (R) of the envelope and thermal capacitance (C) of the interior mass. The differential equation: C * dT/dt = (T_out - T_in)/R + Q_heating. The solution gives indoor temperature over time as a function of heat input.Contact us for a preliminary assessment of your project. Request a demonstration of the system on your data — we will show the savings potential on real figures.
Timeline: basic forecasting system + automatic temperature curve — 6-8 weeks. Full system with RL-based ITP control and leak detector — 4-5 months.