ENERGY · SUNSPEC MODBUS
2,461 mappings·1,027-field schema·ISA-95 categorized·deterministic
SunSpec is already a standard, which is exactly why the gaps in it are dangerous: the register names are fixed, the scale factors are not applied for you.
SunSpec fixes register names, so AC_Power means the same thing on a Fronius and an SMA. What it does not fix is scale: a raw register carries an integer and a separate scale-factor register carries the exponent. Read the value without the factor and you are off by a power of ten, silently, with a plausible-looking number.
The second trap is AC versus DC. On a storage device, A is battery current and genuinely DC, while ACCurrent on the same device is AC line current. Tags that differ by three characters describe opposite sides of the inverter, and a data model that collapses them produces confident nonsense.
Every scale factor applied is reported in the response, with the exponent and which register supplied it, so a 100x correction is reviewable rather than an unexplained number change.
| Raw vendor tag | Canonical field | ISA-95 category | |
|---|---|---|---|
| AC_Power | → | inverter_output_kw | energy_consumption |
| ACCurrent | → | line_current_a | electrical_measurement |
| DCA | → | dc_current_a | electrical_measurement |
| DCV | → | dc_voltage_v | electrical_measurement |
| AC_Frequency | → | grid_frequency_hz | electrical_measurement |
| AC_Energy_WH | → | energy_delivered_kwh | energy_consumption |
| TmpCab | → | inverter_cabinet_temp_c | equipment_condition |
Every row above is produced by the shipping engine, not written by hand. This page fails to build if any of them stops resolving to the field shown.
Resolution is a lookup, not an inference. A tag is matched against a curated corpus of 2,461 confirmed vendor mappings; only if that misses does it fall to weaker layers: a normalized fold that strips unit suffixes and separators, then a signal classifier that reads structure and unit evidence. Each layer carries a lower confidence, and the layer that answered is named in the response. A mapping you disagree with is visible rather than buried.
Below a confidence threshold the engine abstains. An evidence gate refuses a match it cannot justify and reports the tag unresolved, by name, with the score and the reasons, because a wrong field at high confidence is far more expensive than an honest miss. Six read-time validators then check what survives: sentinel detection, physics bounds, stuck sensors, dropout, operating mode and correlation.
Units are converted to SI where the source unit is known, and every conversion is reported with its factor. A value that fails a physics bound is nulled with the bound it violated attached, not silently dropped.
It does not invent fields. A tag outside the corpus that the signal layer cannot justify comes back unresolved and counted, and those names feed the roadmap for the next pack rather than a plausible guess in your data. It is a translation layer, not a historian: it holds no time series and replaces no SCADA system. And the container is a sandbox: it ships the deterministic layers and the canonical schema, not the production embedding, research and corpus-learning layers.
No key, no account, no sales call. The sandbox runs locally and answers with the same canonical schema production does.
The energy vertical resolves at 100% coverage on SunSpec payloads. Send a raw Model 103 block with its scale factors and check the arithmetic yourself.
Every conversion is shown, not asserted. See platform licensing for embedding the canonical data model in your own product, or pricing for usage-based API access.