Artificial Intelligence-Driven Battery Optimisation
Machine learning and artificial intelligence forecast your site’s load, energy prices and the weather. An optimisation engine then decides, every day, exactly when your battery should charge and discharge.
A static rule leaves value on the table.
A standard battery follows one simple rule: charge only from surplus solar, discharge only to cover the site’s load. It never looks at prices, tomorrow’s weather or when the monthly peak is likely to happen.
Optimisation replaces that fixed rule with a plan that is made for each site, each day, and keeps adjusting as conditions change.
Forecast. Optimise. Control. Measure.
- 01Forecast
Machine learning and artificial intelligence models forecast the site’s load, energy prices under its tariff or supply contract, and the weather, and from it the solar generation.
- 02Optimise
A mathematical optimisation engine balances every technique available to the site and finds the schedule that saves the most on that particular day.
- 03Control
The schedule is carried out by the edge controller at the site, which responds in real time.
- 04Measure
Monthly reports compare the actual energy cost with what a standard self-consumption battery would have cost.
Four techniques, balanced every day.
Peak shaving
Predicts when the site’s peak will occur and stores energy beforehand, reducing demand and capacity charges.
Load shifting
Moves energy use from expensive periods to cheaper ones under time-of-use and contract tariffs.
Energy arbitrage
Where the tariff or contract allows, charges when energy is cheap and exports when it is valuable.
Smart export management
Limits solar exports when exporting earns little, nothing or costs money.
Energy flows through the day.
Select a time of day to see how an optimised battery stores surplus solar, supplies the site at the evening peak and keeps a reserve overnight.
Illustrative Illustration of energy flows. Results depend on each site's equipment, tariffs and usage.
Measured against a clear baseline.
Using the site’s minute-by-minute data, the platform reconstructs what a standard self-consumption battery would have done over the same period. The difference between that baseline and the actual bill is the value of optimisation, reported every month so it can be checked.
From the first site to the whole portfolio.
- 1Data
Site, equipment and tariff details, with historical energy data.
- 2Strategy
Desktop analysis to find the optimisation strategy that suits the site.
- 3Commission
Install, commission and complete acceptance testing.
- 4Scale
Repeat the proven approach across the rest of the portfolio.
Frequently asked questions
Do we need access to a wholesale electricity market?
No. Optimisation works with time-of-use and contract tariffs; wholesale-linked strategies are used only where the supply contract allows them.
How are the savings calculated?
Against a standard self-consumption baseline rebuilt from the site’s own data, and reported every month.
Are savings guaranteed?
No. They depend on each site’s equipment, tariff, usage and weather, and are estimated for your site during the strategy stage.
Let's build a smarter, more sustainable energy future.
Tell us about your sites and goals. Our team will come back with a clear next step.