Transforming Smart Buildings with AI: From Theory to 30% Energy Savings

AI-Driven Smart Buildings: From Theory to 30% Energy Savings

We often deploy AI in buildings where the BAS already struggles with peak loads. A typical request: "Heating costs increased by 40%, yet people complain about cold rooms near windows." Classic PID controllers follow rigid schedules, ignoring actual occupancy. Our solution is an RL agent that recalculates setpoints every 15 minutes based on sensor data, weather, and tariffs.

Project results consistently show 20–35% savings on HVAC and lighting while improving comfort. Average savings reach up to $30,000 per year for a 10,000 sq ft building. Below is how it works at the subsystem level.

How the RL Agent Works for HVAC

The RL agent trains on historical data: occupancy, weather, electricity prices, and building thermal inertia. It uses Deep Q-Learning with a neural network of 3 hidden layers Mnih et al., 2015. In production, the model runs on Kubernetes with GPU inference — p99 latency <50 ms. The full training cycle takes 2–3 weeks on 10,000+ data points. To accelerate convergence, we apply prioritized experience replay and dueling network architecture. This allows the agent to reach 95% of the optimal policy within two weeks of training. We use TensorFlow for model development and deploy on NVIDIA Jetson edge devices.

Why Sensor Fusion Matters

Using only one occupancy source (e.g., CO2) reduces prediction accuracy by 30%. We combine three or more: CO2 sensors, Wi-Fi presence counters, and thermal cameras. This yields an occupancy map with 95% accuracy, enabling the RL agent to predict load one hour ahead precisely. In one project, data fusion with a Kalman filter lowered occupancy forecast error from 25% to 5%.

How AI Outperforms Traditional BAS

Key metric comparison: AI achieves 25–30% HVAC savings vs. 0% for traditional BAS — that's at least 25% better. Temperature accuracy is ±0.2°C vs. ±0.7°C, which is 3.5 times more precise. Real-time occupancy adaptation replaces fixed schedules. The table below summarizes:

What's Included in the Work

1. BAS audit: inventory of controllers, sensors, actuators.
2. Historical data collection: occupancy logs, energy consumption, weather data for the last 12 months.
3. ML model development and training: RL agent, occupancy prediction (LSTM/Prophet), anomaly detection (Isolation Forest).
4. Integration with BAS via BACnet/IP or Modbus — without replacing existing equipment.
5. Commissioning and calibration: achieving target KPIs, fail-safe configuration.
6. Documentation handover, facility staff training, and 3-month support.

Deployment Process

Total timeframe: 12–20 weeks. Cost is calculated individually based on building area and number of control loops.

Typical Mistakes in Automation

• Using only one occupancy source (e.g., just CO2) — reduces prediction accuracy by 30%. We combine 3+ sources.
• Ignoring tariff structure — an RL agent without energy cost consideration is suboptimal. We load dynamic tariffs.
• Lack of fail-safe — on AI model failure, BAS must switch to a backup PID. We design architectures with manual override.

Results and Guarantees

Our certified engineers have 10+ years of experience in MLOps and Building Automation. We guarantee achieving the stated savings targets — otherwise, we refine the system at no extra cost. Every project concludes with documentation, access handover, and facility staff training.

Assess the AI potential for your building — contact us for a free audit. We will find the optimal solution for your budget and timeline. Request a preliminary consultation — we will show what data is needed and what savings to expect.

Edge AI and Optimization: How to Deploy Models Without Cloud?

Imagine: your face recognition model has 4 seconds latency on Jetson Orin, the battery runs out in an hour, and the model crashes with OOM. We are a team of Edge AI engineers with 5+ years in production — we have optimized over 150 models for edge devices. Without profiling and proper choice of quantization or distillation, the project is doomed. The gap between research code and edge deployment is a separate engineering discipline; we help you master it in 2–16 weeks turnkey. Edge AI and model optimization services are not just export, but systematic work with hardware.

Why Simply Exporting a Model Doesn't Work?

A PyTorch model with float32 and batch_size=32 is not ready for edge. Typical problems:

• ResNet-50 in fp32 occupies 98 MB, inference on Cortex-A78 — 380 ms. After INT8 quantization via torch.ao.quantization — 24 MB, 95 ms. Export to ONNX + TensorRT on Jetson — 28 ms.
• YOLOv8m on Raspberry Pi 5 in fp32 — 2.8 fps. TFLite INT8 — 9.4 fps. With XNNPACK delegate — 14 fps (1.5× faster than pure INT8).
• Transformer encoder on mobile CPU: MobileBERT in fp16 via CoreML on iPhone 15 — 18 ms/inference. distilbert-base-uncased in ONNX — 42 ms.

The problem is not choosing "quantize or not" — the right path is determined by the device, task, and acceptable metric degradation. We offer an assessment of your project: within 24 hours we will tell you how feasible it is to speed up the model.

How to Choose Quantization Method for Your Task?

PTQ (Post-Training Quantization) — a quick path. Take a trained model, run a calibration dataset (200–1000 samples), get INT8 or INT4 weights. Tools: torch.ao.quantization, ONNX Runtime quantization tool, bitsandbytes. Accuracy degradation: 0.5–2% on classification. Red zone — small object detection and segmentation, where PTQ gives -4–8% mAP.

QAT (Quantization-Aware Training) — training with simulated quantization noise. More expensive (retraining), but degradation 0.1–0.5%. Justified when PTQ is unacceptable. In PyTorch — torch.ao.quantization.prepare_qat().

GPTQ / AWQ — for LLMs. AWQ better preserves quality at 4-bit quantization. llm-compressor from Neural Magic or autoawq are the main libraries.

Potential savings from choosing the right method can be substantial — for example, reducing cloud inference costs by up to 70% when deploying to edge. Project cost is calculated individually based on model complexity and target platform.

When to Use Pruning vs Distillation?

Structural pruning removes channels or layers. torch.nn.utils.prune — basic tool. For transformers — attention head pruning (LTP, movement pruning). Result: ResNet-50 after removing 40% of channels with fine-tuning — -35% size, -28% latency, -1.2% top-1 accuracy.

Knowledge distillation — train a small student to mimic a large teacher. Classic via KLDivLoss on soft labels. Feature distillation on intermediate layers is more effective. Hugging Face DistilBERT: 66M vs 110M parameters, -40% latency, -3% on GLUE. This is a model compression technique.

Combined approach: distillation → pruning → QAT. Gives maximum effect on limited hardware. We recorded a case where a client achieved 70% reduction in cloud compute spend after moving to edge with this pipeline.

Target Platforms and Tools

TensorRT — the main tool for NVIDIA edge. TRT builds a graph with operator fusion, selects optimal kernels. On Jetson AGX Orin YOLOv8m in TRT INT8 gives 78 fps vs 22 fps in fp16 PyTorch — 3.5× improvement.

Practical Case: How We Detected Defects on a Production Line (Our Client)

Task: real-time scratch detection on metal, 30 fps, camera to Jetson Xavier NX (16GB). Original model YOLOv8l mAP50 0.91, server inference 28 ms, on Jetson in fp16 — 110 ms (9 fps). Not suitable.

Optimization steps we performed for our client:

1. Switch to YOLOv8m — mAP50 0.887 (-2.3%), 68 ms
2. Export to TensorRT FP16 via yolo export format=engine half=True — 31 ms (32 fps)
3. INT8 calibration on 500 frames — 22 ms (45 fps), mAP50 0.879

Result: 3.5% degradation at 5× speedup. Client received engine and documentation. We guarantee metric will not drop below agreed threshold — specified in contract.

What is Included in the Work?

• Report on model profiling on target device (layer latency, bottlenecks)
• Selection and justification of optimization methods (quantization / pruning / distillation)
• Optimized model (TensorRT engine / TFLite / CoreML / ONNX)
• Configs for reproducibility (scripts, Docker image, instructions)
• Testing on real device (at least 10,000 inferences)
• Training of your team (2 hours online)
• 1 month support after delivery

How to Order Model Optimization

1. Submit a request on the website or contact us in any convenient way.
2. We perform free profiling of your model on the target device within 24 hours.
3. We prepare an optimization plan with trade-off estimates (speed vs quality).
4. You approve the plan — we start work.
5. After completion, we deliver the optimized model, configs, and documentation.
6. We train your team and provide monthly support.

Timeline: optimization of an existing model — 2–4 weeks. Development from scratch for edge — 6–16 weeks.

Get a consultation — we will evaluate your model for free and offer a plan within 24 hours. Order free profiling now. For complex projects, contact our engineering team to discuss custom optimisation strategies.