Evaluating AI for energy management
Start with the decision
An energy-management model should support a specific decision: when to charge, how to allocate a limited power budget, or when to flag an unusual demand pattern. Define the decision and the consequences of an error before choosing an algorithm.
Build a fair baseline
Compare an AI approach with a simple control rule or existing process under the same data and operating constraints. Separate training and evaluation data, and test periods with unusual demand or missing measurements.
Measure more than prediction error
Forecast accuracy is only one part of the problem. A control system also needs to respect asset limits, behave predictably when data is unavailable and allow appropriate human intervention. Simulation can reveal failure modes before a field trial.
Keep the conclusion proportional
A promising result in a simulator is evidence about that model and dataset. It is not a guarantee of hardware performance, savings or operational safety. Document the conditions, limitations and further tests needed.
Let’s discuss your next challenge
Tell us about the problem, your operating environment and what success would look like. We can discuss a suitable research or development approach.