03 · Optimise
AI as substance, not as slogan.
"AI-powered" is now table-stakes in energy software. We don't lead on it. We let the features prove it.
Every model in Wia is grounded in your actual consumption curve - not synthetic profiles, not industry averages. Anomalies surface the moment they happen, not in the next monthly report. Savings opportunities arrive ranked by payback, with a confidence band, so your team knows what to chase and in what order.
How Optimise works
Learn. Detect. Act.
01
Learn
Once a meter has 24-72 hours of data, Wia builds an expected-range model for it - informed by weather, working hours, tariff windows and (where relevant) production schedules. The model retrains automatically.
02
Detect
Anomalies surface the moment a reading falls outside the expected band. Savings opportunities are mined continuously from the data - ghost loads, schedule drift, peak demand, vampire load.
03
Act
Each finding lands in a ranked queue with annual saving, payback and confidence. Your team triages, assigns, fixes. Measured savings are tracked back to prove the intervention worked.
Inside Optimise
Everything Optimise does, in one place.
Savings Opportunities
Per-technique breakdown - vampire-load, peak-shaving, schedule-drift, HVAC. Each ranked by annual saving, payback and confidence band so your team knows what to chase first. Marked done in-app, with measured savings tracked afterward.
Solar Yield Optimiser
Solar modelling against your actual load shape, plus an operational queue of actions to capture more on-site generation. Curtailment alerts when production exceeds load. Compares modelled yield to actual every period.
Charging Optimisation
Schedule batteries and EV fleets to capture cheap tariff windows and on-site generation. Tariff-aware, weather-aware, with manual override for known events. Outputs a daily charge schedule per asset.
Anomaly Detection
Expected-range bands per meter per period. Deviations surface the moment they happen - pushed to alerts, never waiting for the next report. The bands adapt to your operating pattern over time.
Forecasting
Bills become forecasts. Consumption projections by site and utility, refreshed daily, with confidence intervals you can plan around. Drives capex models and budget vs actual variance reporting.
Site Comparison
Like-for-like across the portfolio. Top performers ranked automatically; outliers surface for investigation. Cuts the time from "where do we focus?" to "fix these six sites." Like-for-like respects floor area, opening hours and climate.
Battery ROI
Battery sizing against your actual consumption curve, not synthetic profiles. Modelled against tariff windows, peak charges and known capacity-market values. Outputs a sizing recommendation and a payback curve.
Capex Modelling
Solar sizing, retrofit modelling, lighting upgrades - investments against actual data, not assumptions. Each project lands with a payback, IRR and confidence band. Compare scenarios side by side before committing.
Ghost-load Detection
Always-on equipment drawing power outside operating hours. Surfaced automatically and tracked over time so you can prove the savings after intervention. Tied into the Manage tasks workflow.
Savings opportunities, ranked
A queue, not a dashboard.
Most "AI energy" tools surface insights and stop there. Wia delivers a ranked queue - every opportunity with annual saving, payback and confidence band, scoped to a specific site and a specific technique. Your team works from the top of the queue. Below: real opportunities surfaced across a logistics portfolio.
Each closed opportunity is tracked for 12 months to prove the savings actually landed.
Works with
The stack your team already runs.
Optimise outputs are designed to drop into the systems your team works in.
Ticketing & ops
- Jira
- ServiceNow
- Asana
- Linear
- Slack
Finance & ERP
- SAP
- Oracle
- NetSuite
- Sage
- Xero
Forecasting & BI
- Anaplan
- Adaptive
- Power BI
- Tableau
- Looker
- Snowflake
FAQ
Common questions
How long until the AI is useful?
Anomaly detection works from day one against simple baselines. Savings opportunities and forecasting need 4-12 weeks of data to reach high confidence - the platform marks confidence on every output.
Will the AI ever be wrong?
Yes - that's why every output carries a confidence band. Your team triages findings, marks false positives, and the model retrains. We track precision per technique per customer.
Do you use LLMs?
No. Optimise is built on statistical models, time-series forecasting and physics-aware baselines. LLMs are wrong about energy in ways that matter - we don't use them for any forecasting or anomaly work.
Can we export the underlying models?
You can export every input, every output and every parameter via the API. The model structures themselves are documented; the trained weights are exportable on enterprise contracts.
Want to see this on your data?
Bring your portfolio - we'll spin up a live view in the demo.
Request a demo