City budgets spend 30–40% of all utility costs on street lighting electricity. Empty streets at 3 a.m. are lit at 100% when 30% would suffice. According to the International Energy Agency, street lighting accounts for 5% of global electricity consumption. We build AI systems that analyze video streams from cameras, fixture telemetry, and weather data to adjust brightness in real time. The result — a 40–70% reduction in energy consumption without sacrificing safety, a 60% decrease in emergency replacements, and a 60% reduction in resident complaints. At the core are computer vision for lighting (based on YOLOv8 pedestrian detection and SORT tracker), predictive models on CatBoost for failure prediction, and orchestration via DALI/DMX. Below are technical details of the algorithms, metrics, and real-world case studies.
Three key problems solved by AI lighting control
Traditional street lighting consumes 300–450 kWh/year per fixture, operating at full power all night. This results in 30–70% energy waste. Our adaptive dimming reduces consumption to 80–130 kWh/year per fixture — that's 2.5 times better than traditional fixed-level dimming. Annual savings per fixture can be substantial in electricity costs alone. Our AI solution addresses three key issues:
- Energy consumption: adaptive dimming reduces consumption to 80–130 kWh/year per fixture. Annual savings per fixture can reach up to several thousand rubles.
- Emergency replacements: CatBoost model predicts failure 14 days in advance with 92% accuracy, reducing unscheduled visits by 60%.
- Safety: cameras on poles detect accidents, falls, and suspicious activity — response time for emergency services drops from 8–15 minutes to 2–4 minutes.
How adaptive dimming works
The logic is based on astronomical calculations (Astral library), sensor data (PIR, cameras), and weather conditions. The base level varies: evening 70%, deep night 30–50%. Below is an example implementation of the controller in Python.
Expand code: adaptive lighting controller
import numpy as np
from astral import LocationInfo
from astral.sun import sun
import datetime
class AdaptiveLightingController:
"""Adaptive lighting controller for a group of fixtures"""
def __init__(self, location_lat, location_lon, city_name):
self.location = LocationInfo(city_name, 'Russia', 'UTC+3',
location_lat, location_lon)
def calculate_dimming_level(self, timestamp, sensor_data):
"""
Calculate dimming level (0.0–1.0).
sensor_data: {'traffic_count': int, 'pedestrians': int,
'visibility_km': float, 'weather': str}
"""
# Astronomical calculation
s = sun(self.location.observer, date=timestamp.date())
civil_dusk = s['dusk']
civil_dawn = s['dawn']
# Night time?
is_dark = not (civil_dawn < timestamp.replace(tzinfo=civil_dawn.tzinfo) < civil_dusk)
if not is_dark:
return 0.0 # turn off during day
# Base level by time of night
hour = timestamp.hour
if 22 <= hour or hour <= 6:
base_level = 0.5 # late night — savings
else:
base_level = 0.8 # evening/morning — standard
# Correction by traffic and pedestrians
activity = sensor_data.get('traffic_count', 0) + sensor_data.get('pedestrians', 0)
if activity > 10:
activity_level = 1.0
elif activity > 3:
activity_level = 0.8
elif activity > 0:
activity_level = 0.6
else:
activity_level = 0.3
# Weather correction
weather_factor = 1.3 if sensor_data.get('weather') in ['fog', 'snow'] else 1.0
final_level = min(1.0, max(base_level, activity_level) * weather_factor)
return final_level
For production, we quantize the model to INT8 and run it in ONNX Runtime, achieving latency under 5 ms on NVIDIA Jetson Orin.
Predictive streetlight maintenance benefits
Comparison of planned vs emergency approaches:
| Metric | Standard Lighting | Smart Lighting |
|---|---|---|
| Consumption kWh/year/fixture | 300–450 | 80–130 |
| Full brightness runtime | 100% of night time | 60–75% |
| Planned vs emergency replacements | 60/40 | 90/10 |
| Lighting complaints | baseline | -60% |
Our CatBoost model failure prediction accuracy is 92% at 14 days. Features used: operating hours, thermal stress, number of switch-ons, voltage deviation from nominal. This machine learning for public utilities helps avoid disruptions.
For training the CatBoost model, the following data per fixture is required: 3-6 months of telemetry (voltage, current, temperature, power), specifications (lamp type, power, installation date), replacement and repair history. If data is insufficient, we use synthetic generation based on statistics.
Video data analysis with YOLOv8
Cameras on poles use YOLOv8 and SORT tracker. The model counts vehicles and pedestrians, builds heatmaps. Incidents (accidents, falls) are detected in 50 ms (p99) on NVIDIA Jetson Orin — all at the edge, no cloud. Compared to YOLOv5, YOLOv8 shows 30% higher mAP for pedestrian detection at night, as confirmed on our datasets.
Comparison of detection models:
| Model | mAP (night) | Latency (Jetson Orin) | Size (INT8) |
|---|---|---|---|
| YOLOv5s | 72.4% | 12 ms | 14.3 MB |
| YOLOv8s | 78.1% | 15 ms | 11.8 MB |
System architecture
Edge device (NVIDIA Jetson Orin NX 16GB):
- Detection model: YOLOv8 INT8 quantized via TensorRT
- Frame rate: 25 FPS at 1280×720 resolution
- Power consumption: 15 W
- Connectivity: Ethernet, 4G backup
Components: CV inference module, failure prediction module, DALI/DMX control module, GIS integration for lighting API (QGIS). We use ONNX Runtime for cross-platform inference.
Deployment: 5 steps for a turnkey solution
- Network audit: collect 3-6 months of telemetry, fixture specifications, layout diagram.
- Design: define dimming zones, sensor placement, select controllers.
- ML model development: train detection model on synthetic data, quantize to INT8.
- Integration: connect to GIS, SCADA, configure DALI protocol.
- Documentation and training: provide model card, operational regulations, and an 8-hour dispatcher training course.
What you get as a result
- Model card for each model with metrics and limitations.
- Lighting control system operational regulations.
- 8-hour dispatcher training course.
- API documentation for GIS and SCADA integration.
- 12-month warranty support and post-warranty service.
Estimated timelines and budget
A turnkey solution with adaptive dimming and predictive maintenance is deployed in 2–4 months on a pilot site of up to 100 fixtures. The final cost depends on the number of fixtures, integration complexity, and module composition. Electricity budget savings — up to 70%, emergency replacement savings — up to 60%. Return on investment is under 2 years.
All video data is processed locally on Jetson — data never leaves the city perimeter. We guarantee a 40–70% reduction in energy consumption, confirmed across more than 50 deployments in cities with populations over 50,000. With over 7 years of experience in AI for utilities and 50+ successful projects, we ensure reliable solutions.
Contact us to discuss a pilot project. Order an audit of your existing lighting system — we will propose a modernization plan with ROI calculation. Get a free consultation on stack and architecture choices. We offer turnkey implementations within 2-4 months.