· Energy · 2 min read
Energy simulations with reproducible scenarios and data pipelines
How to keep simulation results trustworthy across teams and time.
Energy simulations are only useful if results are reproducible. If two teams cannot repeat a run and get the same output, trust in the model will erode quickly.
Start with data provenance. Track the source, version, and timestamp of every input dataset. Make it easy to answer which data produced a given result. This is critical when regulatory questions or operational disputes appear.
Scenario configuration
Store scenario settings in version control and keep them as explicit configuration files. Avoid ad hoc scripts and undocumented parameters. If a scenario changes, capture why and who approved it.
Deterministic execution
Control random seeds and pin dependencies. Run simulations in controlled environments with a standard runtime image. If results change between runs, fix the pipeline before adding new features.
Outputs as artifacts
Store results with metadata that links to inputs, code, and configuration. Provide a short summary that includes the scenario name, run ID, and headline metrics. This reduces back and forth when teams compare runs.
Include checks for missing or stale inputs. A simulation that runs on incomplete data can look correct while being wrong. Add validation and fail fast when required inputs are missing.
Reproducibility is an operational feature. It enables trust and makes simulation results usable in real decisions.
Use a standard run template for all simulations. A consistent entry point and parameter set makes automation easier and reduces errors. It also makes it easier to compare runs across teams.
Keep a small set of reference scenarios that act as regression tests. When the pipeline changes, rerun these scenarios to check that results stay within expected ranges.
Document the boundaries of the model. If the simulation does not cover a specific market condition or asset type, state that clearly in the output. This avoids misinterpretation when results are shared.
