AI-Powered Analog Meter Digitalization
Instead of costly hardware upgrades, we built an AI-powered retrofit solution that digitalizes analog electricity meters using computer vision. This enabled near real-time consumption data, improved demand forecasting, and opened up new trading opportunities on the energy market.
Intro / Context
Many industrial companies still rely on legacy analog meters to measure electricity consumption. These devices require manual readings, often only once a day or even once a week, which limits the accuracy of forecasting electricity demand. Upgrading to modern digital meters is possible, but requires significant capital expenditures — making it unattractive for many organizations.
To address this, our hypothetical company explored a retrofit solution: using AI-powered image recognition to transform analog meters into smart, digital data sources at a fraction of the cost.
The Challenge
- Manual meter readings were time-consuming and error-prone
- Low-frequency data (daily or weekly) reduced the accuracy of consumption forecasts
- The company was overpaying for electricity due to poor planning and forecast errors
- High investment costs made upgrading to new hardware infeasible
The Solution
We designed a retrofit system combining affordable hardware and advanced AI:
- Hardware Setup: Small, low-cost devices (e.g. Raspberry Pi + camera) installed directly on analog meters.
- Image Capture: Devices take periodic photos of the meter display (e.g. every 15 minutes).
- AI Image Processing: A custom-trained computer vision model detects and extracts the meter reading digits in real-time.
- Automated Data Flow: The readings are automatically uploaded to the company’s central database.
➡️ This creates a fully automated, near real-time stream of consumption data without replacing existing infrastructure.
The Results
The benefits of this upgrade were immediate and measurable:
- Higher data frequency → more accurate electricity demand forecasting
- Reduced energy costs → the company could buy electricity in advance with better precision
- New revenue opportunities → with near real-time data, the company could:
- Trade electricity on the intraday market
- Participate in control power auctions
- Quick ROI → low upfront costs compared to new digital meters
Where My Demo Fits In
As part of this workflow, I developed a demo system for AI-based analog meter data extraction.
This piece demonstrates the core AI technology: accurately recognizing and digitalizing numbers from analog meter images. In a full solution, this demo is integrated with hardware devices and company databases to enable seamless end-to-end automation.
Beyond This Use Case
Analog meter digitalization is just one example of how AI can bridge the gap between manual, analog processes and digital workflows. Similar approaches can be applied to:
- Gas and water meters
- Factory machinery displays
- Pressure/temperature gauges
- Utility infrastructure (pipes, pumps, grids)
- Other analog indicators where retrofitting digital sensors is costly
By combining computer vision + workflow automation, companies can unlock hidden efficiencies and profitability without major capital investments.
