AI Energy Cost Agent

Executive Summary

Energy spend is rising while tariffs become more complex. Without real-time visibility, organizations pay for waste, miss cheaper windows, and trigger avoidable peaks. The AI energy cost agent monitors meter and sub-meter data, learns patterns per site and device, detects anomalies, and recommends or automates measures such as load shifting, shutting down standby consumers, and tariff optimizations. Facilities and operations teams retain control through human-in-the-loop approvals and clear dashboards. Results are auditable and feed sustainability/ESG reporting.

The problem today

Most sites rely on monthly invoices and periodic audits. Peak charges go unnoticed until billing. Night and weekend baseloads drift upward; equipment schedules diverge from actual occupancy; tariff changes are not exploited. Manual analysis cannot keep pace with thousands of data points across devices, lines, and locations.

The AI-led flow

1. Ingest & normalize: Pull high-frequency data from meters, sub-meters, BMS/SCADA, and IoT sensors; normalize to a canonical schema with weather and calendar features.
2. Forecast & baseline: Train time-of-day/seasonal models per site and device to predict expected load; quantify uncertainty to reduce false alarms.
3. Detect & explain anomalies: Surface unusual baseloads, weekend spikes, simultaneous heating/cooling, drifting standby consumption, or failing equipment—each with explainable reason codes.
4. Optimization & actions: Recommend load shifting (pre-cooling/heating), staggered equipment starts, standby shutdowns, and demand-charge avoidance. Where permitted, execute automations via BMS APIs or smart switches; otherwise request one-click human approval.
5. Tariff intelligence: Match consumption profiles to cheaper tariffs or time-of-use windows; simulate savings before changes.
6. Dashboards & assistant: Provide site/device views, peak forecasts, savings trackers, and ESG metrics. A chat assistant answers, “Which three sites had unnecessary weekend baseload last month?”
7. Governance: Role-based access, audit logs for every recommendation/action, rollback options, and measurement & verification (M&V) to attribute savings.

Privacy-by-design, compliance-aligned: Minimal necessary operational data, role-based access, region-bound processing (e.g., EU), immutable logs, and clear opt-in for automated controls. This is decision support; humans approve policy-relevant changes.

Pilot scope (30–45 days)

• Scope: One facility (or cluster), 10–20 meters/sub-meters, focus on weekday peaks and off-hours baseload.
• Interfaces: Read from smart meters/BMS; optional write-back for a limited set of safe automations (e.g., non-critical HVAC schedules).
• Success criteria: Peak demand alerts precision/recall, baseload reduction, avoided peak charges, and estimated € savings with M&V.

Hypothesis metrics (illustrative, not guaranteed):

• Baseload reduction 5–10% in off-hours within the pilot zone.
• Peak clipping 10–20% on targeted events via staggered starts/load shifting.
• Tariff optimization adds 1–3% savings where alternatives exist.

Quick ROI math (scenario):
Annual electricity spend €2.0M. A conservative 5% reduction yields €100k/year. If implementation/ops cost €40k–€60k/year, payback is often < 12 months—before tariff changes or demand-charge avoidance add further gains.

Risks & mitigations

• Data quality gaps: Use signal validation, gap filling, and confidence scoring; suppress automations when confidence is low.
• Comfort & process constraints: Encode business rules (temperature, process windows) and require approvals for high-impact actions.
• Integration limits: Start read-only; expand to safe write-backs after M&V proves stability.
• Rebound effects: Track post-event consumption to avoid shifting costs instead of reducing them.

From pilot to scale

Roll out by site and device class, add water/gas where relevant, and integrate with procurement for dynamic tariff switching. Incorporate on-site generation/storage to optimize self-consumption. Feed verified savings into ESG reporting and budgeting so finance sees predictable reductions rather than invoice surprises.

Expected impact (illustrative):

• Immediate cost reduction through consumption optimization and load management.
• Identification of efficiency potentials (devices, locations, times with unnecessary consumption).
• Use of cheaper tariffs and avoidance of peak loads.
• Sustainability & ESG reporting via measurably lower energy consumption.
• Fast ROI (often <12 months) through reduced energy costs and improved cost control.