Save up to 20% per year on your industrial plant's energy costs, without CAPEX or new installations.
The J4Energy Energy Co-Pilot optimizes your plant's configuration in real-time, reducing consumption and emissions without any plant modifications.
Managing an industrial energy plant optimally is a recurring challenge for energy managers, who often encounter one or more of these problems:
External variables such as energy prices, weather conditions, and production constraints make the decision-making process complex.
Plant optimization is managed with Excel sheets, which require manual updates and do not allow simulating dynamic scenarios.
Energy Co-Pilot eliminates these difficulties, providing real-time operational prescriptions to maximize efficiency and savings.
Energy Co-Pilot uses artificial intelligence and advanced models to collect data, simulate configurations, and provide operational guidance in three simple steps.
This allows for complete visibility of consumption, anomalies, and best practices in a single dashboard.
Energy Co-Pilot simulates hundreds of possible energy configurations, integrating all relevant data in real-time:
The software analyzes hundreds of possible scenarios to identify the most advantageous energy configuration based on the company's objectives (e.g., cost minimization, emission reduction, operational efficiency).
Flexible Implementation
Operational prescriptions can be implemented automatically – via IoT hardware – or manually, by the plant’s facility manager.
Energy Co-Pilot uses models and algorithms developed by Stefano Moret, Group Lead at ETH Zurich and winner of the ERC Grant 2024.
For 12 years, Stefano has been studying the application of mathematical models to complex energy systems in the best engineering schools in the world.
Case Study
Energy Co-Pilot
Discover how a large Italian industrial company managed to save 10% on operating costs in 3 months.
Who is the Energy Co-Pilot for?
Energy Co-Pilot is designed for industrial companies that want to reduce energy costs without investing in new technologies.
Reduces energy costs by up to 20%, without impacting the CAPEX budget and without infrastructural modifications.
Provides continuous visibility on energy consumption, ensuring faster and more informed decisions.
Allows integrating energy optimization into corporate sustainability and profitability goals.
Eliminates complex manual activities, making energy optimization faster and more efficient.
Allows dedicating more time to value-added decisions, instead of manual data management.
Dynamic simulations and detailed reports to respond in real-time to market variations.
Detailed analysis of energy consumption
Studies the energy consumption of each plant during all operational phases (start-up, dwell, production, CIP, and shutdown) for a complete and accurate view.
Comparison between consumption and duration
Compares the energy used in each phase with its duration, identifying the most energy-intensive phases and the areas to focus on for optimizing consumption.
Setting optimization objectives
Sets specific objectives to reduce the duration of the most energy-intensive phases, optimizing energy use and improving the overall efficiency of the production process.
Optimized reference model for the long term
Once the optimization model is obtained, Energy Co-Pilot implements it to ensure efficient and sustainable energy management over time.
Continuous monitoring of savings
Constantly analyzes the results and quantifies the savings obtained for continuous improvement of energy performance.
Up to 20% energy savings
Optimizes consumption without investing in new technologies.
Sustainability without compromise
Measurable reduction of CO₂ emissions.
Operational freedom for Energy Managers
Elimination of repetitive activities and focus on high value-added activities.
Energy Co-Pilot adopts an innovative and advantageous pricing model for companies: our compensation is based on a percentage of the energy savings actually generated thanks to our software. In other words, we only earn when you achieve concrete savings.
Savings are calculated from a baseline defined according to the IPMVP guidelines, the most authoritative international protocol for measuring and verifying energy savings. This approach ensures precise and reliable measurement, guaranteeing accurate and verifiable results.
Frequently Asked Questions
What is the cost of the service and how long does it take to recover the investment?
Our model is mainly based on a success fee: we retain only 20% of the savings generated. This means that the initial costs are covered, and the service pays for itself automatically with the savings achieved, calculated from a baseline.
How is the baseline calculated to quantify energy savings?
The baseline is calculated according to the IPMVP (International Performance Measurement and Verification Protocol) by following these steps:
Initial analysis: we evaluate the current state of energy consumption (the “as is” phase).
Metering installation: we implement monitoring tools to collect accurate data.
Monitoring and normalization: we analyze the initial consumption and scale it based on seasonality to obtain a reliable baseline, compliant with IPMVP standards, an international protocol developed to measure and verify the energy savings achieved through energy efficiency interventions.
How do you forecast energy prices?
We use a model based on:
- Historical data from energy markets to reconstruct price trends.
- Machine learning algorithms, leveraging already available data.
What happens if my plant doesn't have Smart Meters, IoT, or BMS/BIM?
If your plant doesn’t already have these technologies, we will assist you in installing the necessary solutions, ensuring a smooth transition without operational interruptions.
Where do you get the external data from?
Our models are based on several data sources, including:
- Consumption monitoring via metering.
- Forecast data on energy prices.
- Bills to reconstruct current and historical prices.
- J4 database for machine specifications.
- Meteorological and irradiance data for photovoltaic and wind energy.
- Local temperatures measured with metering sensors (thermometers).
How are the operational prescriptions implemented?
The implementation of operational prescriptions can be carried out manually by the facility manager or automatically through the IoT technologies present in the plant.
How do you simulate the operation of a physical machine within a plant?
We have developed a model that integrates:
- Efficiency curves and specific outputs for each technology, already implemented in our app.
- Consumption and demand data, which are interpolated to simulate the actual behavior of the machines within the plant.
Please feel free to write to our friendly support team.
