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Yes. J4Energy builds on more than 12 years of energy research at ETH Zurich. By combining AI with proprietary algorithms, it simulates complex scenarios with very high accuracy. It has already been tested and deployed by large companies such as EGO (Shell Group), Electrolux and Gruppo Florence.

J4Energy’s Sales Co-Pilot has a simple, intuitive interface designed for people without a technical background. We also offer tailored support and training at every stage, for a fast, smooth rollout so users benefit from the results from day one.

The guided data entry asks for a few simple details: • Plant location, working days and NACE code • Energy services. For each selected service you can enter daily profiles or define your own. • Energy resources with their consumption and costs. You can upload your own profiles or scan the energy bills directly • Currently installed technologies and their sizes (where known) The simulation that follows delivers accurate, reliable results even when some of this data is missing. How? Where data is scarce, the tool uses synthetic data generated specifically for that scenario. For the plant’s day-to-day operation, the following are added: • The production plan • Work shifts • Field data from monitoring systems, where available These are complemented by energy price forecasts, weather and irradiance data for solar and wind, and the J4Energy database of machine specifications.

Absolutely. The tool already offers a very large library of the best technologies on the market, and you can easily add the ones you are interested in (respecting your privacy) if they are missing. For each new technology you only need: capex, maintenance costs, lifetime, capacity factor and max capacity. For the plant’s day-to-day operation, where no monitoring systems are in place, J4Energy helps its clients install the solutions needed, for a smooth transition without operational downtime.

The initial analysis, after the guided data entry, takes 3 to 5 minutes. The output is the ‘as-is’ state (energy analysis) of the plant. Two simulations are also created automatically, the cost-optimised scenario and the maximum emission reduction scenario: the first favours a technology configuration that optimises costs, the second maximises greenhouse gas savings, with a larger investment and longer payback. Each further simulation created with the tool takes about 5 minutes to produce a complete, accurate result.

The analysis and the simulations the tool proposes are highly accurate: average accuracy is 97% and never falls below 90%, across more than 10,000 analyses compared with the traditional ones carried out by our clients. To show the accuracy and level of detail the tool can reach, we take on an analysis that has already been validated (black box) and run it through the tool ourselves. We then show the result, so the client can see how accurate and reliable the tool is.