Campus energy brief separates forecasting capability from projected operating savings

Ask the facilities director, energy manager, finance team and campus IT which demand problem is expensive enough to investigate. What metering exists, and which comfort or laboratory constraints cannot change? A bounded engagement could assess one building's data readiness and define a shadow forecast trial. The value hypothesis is better planning before equipment control is automated. Treat the brief's estimated savings as a question for local measurement. Do not promise transferable percentages, a short payback period or reduced staffing.

Require trustworthy timestamps, meter calibration records, occupancy calendars and an approved baseline before choosing a model. Compare a simple schedule-based forecast with the candidate using a later held-out period that includes closures and term changes. Test missing-data handling and isolate the experiment from building controls. Proposed validation should report forecast error by operating regime and then separately test whether an operator action changes consumption without degrading service. Resolve the inaccessible paper's methods before using its performance as a benchmark.

Assign the energy manager operational ownership, with controls technicians and data engineers responsible for integration. Establish a commissioning checklist, incident escalation and a staffed maintenance plan. Train operators using accessible displays and rehearse loss of telemetry. Proposed acceptance requires complete provenance for test data, a documented baseline comparison and a working manual fallback; any later savings claim must use metered intervention results and include total effort. Main risks are seasonal bias, unsafe control changes and an unsupported prototype becoming a critical service.