AI System for Optimizing Energy Consumption in Manufacturing
An industrial plant with 100 MW installed capacity pays €0.15–0.30/kWh for electricity. A 10% reduction saves €13–26M per year. We develop AI systems that not only monitor but automatically optimize energy consumption. Over our work, we have completed 15 projects for plants across various industries — from metallurgy to food processing. Our approach combines multiple methods: LSTM autoencoders for anomaly detection, reinforcement learning (RL) for battery energy storage system (BESS) control, and model predictive control (MPC) for HVAC. Experience shows that without accounting for production specifics, optimization yields at most 3–5%, while a properly tuned ML model achieves 15–20%.
How to Disaggregate Consumption Without Meters on Every Machine
Non-Intrusive Load Monitoring (NILM) is the key technology. One smart meter at the main incoming supply, an ML classifier (transformer or LSTM) based on current/power patterns. Output: which equipment is on and how much they consume. Accuracy for major consumers is 85–95%. No meters on each machine, reducing implementation costs by a factor of 2–3. Additional benefit: NILM reveals unidentified loads, which often account for up to 10% of peak demand.
| Method | Accuracy | Implementation Cost | Setup Time |
|---|---|---|---|
| NILM (ours) | 85-95% | Low (one meter) | 1 day |
| Individual meters | 99% | High (per machine) | Weeks |
According to the International Energy Agency, AI-based energy optimization can reduce industrial consumption by 10–20%.
Consumption Analytics
Baseline Normalization — a model builds expected consumption based on production volume, temperature, and day of week. Deviation from baseline signals an anomaly. SHAP attribution localizes the problematic equipment. For example, at a cement plant we identified a faulty compressor consuming 30% more than normal, and replacement saved €200k per year.
| Metric | Description | Typical Improvement |
|---|---|---|
| SEC (kWh/unit) | Specific energy consumption per product | -10–15% |
| Peak demand | 15-minute max load | -20% |
| Power Factor | Power factor | up to 0.95 |
Why RL Is More Effective Than Rules for Load Management
Demand Response (DR) and Time-of-Use (TOU) are classic tasks, but manual rules do not adapt to changing prices or weather. Reinforcement Learning (PPO/SAC) trains on a shop floor simulator and finds the optimal strategy: when to charge/discharge BESS, when to shift compressible air, how to reduce peak without penalty for downtime. In a pilot project at a chemical plant, the RL agent yielded an additional 5% savings over a rule-based system. Beyond PPO, we use SAC for continuous actions and Hybrid A* for discrete switching.
Peak Shaving — ML predicts the peak 30–45 minutes in advance and commands BESS discharge or reduction of non-priority loads. Demand charge drops by 20%.
Optimization of Compressor Stations and HVAC
Compressors account for 15–30% of a plant's electricity. Optimal scheduling considering efficiency curves, minimum network pressure (each 1 bar reduction saves 6–7%). Predictive leak detection for compressed air — up to 40% losses can be avoided. For HVAC, we use Model Predictive Control leveraging building thermal inertia — savings of 15–30%.
How to Launch a Pilot Project in 2 Weeks
- Collect historical data (one year, hourly) from existing meters.
- Install one meter at the main supply if data is missing.
- Train NILM model on your data.
- Build consumption baseline and assess savings potential.
- Prepare an analytical report with a proposal for full implementation.
Case in point: at a chemical plant, SEC reduction was 12%, peak load decreased by 18%, payback period of 14 months. These results are achieved without production stoppage.
What's Included in the Project
- Analytical report with baseline and savings potential
- NILM disaggregation model
- DR/TOU module with RL agent
- Compressor and HVAC optimizer
- SEC and anomaly monitoring dashboard
- Documentation and staff training
- 6 months post-implementation support
We guarantee SEC reduction of at least 10% over 12 months. We hold partner certifications from NVIDIA and AWS. Over our work, we have built a library of trained models for 15 production types — accelerating deployment by 30%.
We'll assess your savings potential for free. Get a consultation for your facility. Order a pilot project — in two weeks we will prepare a baseline and demonstrate the possible effect on your data.