Machine Learning Prototype for Predictive Energy Optimization
Machine Learning Prototype for Predictive Energy Optimization
| CLIENT | Veolia |
| COUNTRY | France |
| SECTOR | Environmental Engineering, Energy Efficiency, AI/ML |
| SERVICE | Data Engineering, Machine Learning Development, Predictive Modeling, IoT Integration, System Architecture |
Veolia is a global leader in environmental engineering and sustainable resource management, operating complex energy and infrastructure systems across the world.
To enhance energy efficiency and reduce operational costs, the company aimed to harness its extensive IoT and sensor network, collecting temperature, power usage, and environmental data to develop an AI-powered system that could predict energy consumption, detect anomalies, and optimize HVAC performance across its facilities.
Project Description
To advance its sustainability and energy efficiency goals, Veolia engaged DigitSense to develop a machine learning system that predicts HVAC performance and detects anomalies across its building network.
Using historical IoT and sensor data, the system combines advanced data engineering and predictive modeling to forecast energy demand, identify irregular patterns, and recommend optimization strategies.
Designed with a modular architecture, it allows Veolia’s internal teams to refine algorithms, integrate real-time data, and scale the solution across global operations for smarter, data-driven energy management.
Key Deliverables
- Machine Learning Model Development: Built algorithms for energy forecasting, anomaly detection, and consumption pattern analysis.
- Data Engineering Pipeline: Structured and processed large-scale IoT sensor data for reliable, real-time analysis.
- Predictive Analytics Dashboard: Delivered insights into HVAC performance trends and potential optimization opportunities.
- Modular System Architecture: Designed a flexible framework enabling Veolia to enhance and expand models in-house.
- Knowledge Transfer: Equipped internal teams with the documentation and tools to scale and refine the system independently..
Project Outcome
Through its collaboration with DigitSense, Veolia now operates a machine learning system that predicts energy usage, detects anomalies, and optimizes HVAC performance across its global network.
The system transforms IoT sensor data into real-time insights, enabling teams to make informed, data-driven decisions that reduce energy waste and improve operational reliability.
Built with a scalable, modular architecture, it provides the foundation for Veolia’s ongoing AI-powered sustainability strategy, allowing continuous model evolution, deeper data integration, and measurable progress toward carbon reduction and energy efficiency goals.