AI and machine learning are transforming many industries. In building energy management, the reality is more nuanced than the marketing suggests.
Grounded perspective: Most buildings waste 15-30% of energy due to basic operational failures — wrong schedules, stuck dampers, sensor drift. No amount of AI will fix these if the fundamentals aren't in place. Start with the basics; layer in ML where it genuinely adds value.
Where AI Genuinely Helps
Anomaly Detection: ML models excel at learning normal building behavior and flagging deviations. This is arguably the highest-value application — catching faults that rule-based systems miss.
Load Forecasting: Neural networks can predict next-day or next-hour consumption with useful accuracy, enabling better demand response participation.
Occupancy Prediction: Using Wi-Fi connections, badge data, or CO2 sensors, ML models can predict occupancy patterns to optimize HVAC schedules.
Data requirements: ML models need 12-24 months of clean interval data before they can reliably detect anomalies. Deploying them on sparse or noisy data produces more false alarms than genuine insights — which erodes operator trust quickly.
Where Traditional Methods Still Win
Weather-Normalized Baselines: ASHRAE regression models are well-understood, statistically transparent, and accepted by regulators. ML models may fit better but lack interpretability.
M&V Calculations: IPMVP requires transparent, auditable calculations. A neural network that produces a savings number but can't explain how it got there won't pass scrutiny.
Simple Scheduling: If your building runs HVAC on weekends when no one's there, you don't need AI — you need a corrected schedule.
EdiMono's Approach
We use AI where it adds genuine value: - Intelligent fault detection that learns building-specific patterns - Natural language querying of energy data through our AI agents - Automated model selection for regression analysis
And we use proven methods where they're more appropriate: - ASHRAE-compliant regression for baselines - IPMVP-standard calculations for M&V - Deterministic rule engines for known fault signatures
Key Takeaway
The interpretability of your energy analytics matters as much as their accuracy. If a model flags a fault or claims savings, your engineers need to understand why — to verify it, explain it to clients, and defend it to regulators. Black-box models fail this test.
Key Takeaway
The best energy management platform isn't the one with the most AI — it's the one that uses the right tool for each job.