Learned Encoders

Status: 🔬 Research. Encoders are a v2 research direction. V1 uses raw NWP features directly.

In the full graph-structured architecture, many components need to transform the same raw inputs — NWP grid values, timestamps, substation location — into useful representations. Rather than re-learning these transformations independently inside each node type, shared encoder modules learn a single compact embedding that every node can use.

Why encoders are a natural fit with differentiable physics

This is the key insight: the differentiable physics layer handles DER-specific physical relationships (how much irradiance a panel converts to power given its geometry; how wind speed maps to turbine power via the cubic law). This means the weather encoder does not need to learn anything about solar panels or wind turbines — it just needs to produce a good representation of the atmospheric state. The physics layer then interprets that shared representation through the appropriate equations for each DER type.

The practical payoff: a single shared weather encoder can be trained jointly across all DER types and all substations, benefiting from the full dataset. The encoder learns weather; the physics handles DER specifics.

Encoder types

WeatherEncoder

Maps NWP grid-cell values at a given (location, time) to a compact weather embedding.

Candidate inputs: temperature, wind speed and direction, cloud cover, GHI, DNI, DHI, humidity, pressure. May need to capture temporal context (a persistent pressure pattern carries different meaning than a transient one) and spatial context across nearby NWP grid cells.

Training signal: the reconstruction error from the full forward model — if the weather embedding is poor, the DP modules cannot reconstruct observed substation power correctly.

TimeEncoder

Maps a timestamp to an embedding capturing periodic structure: time-of-day, day-of-week, month, bank holidays, UK calendar effects (Christmas, Easter). Shared across all node types since every component of the forward model has some time-of-day / time-of-year structure.

SpaceEncoder (possible future addition)

A static per-substation embedding capturing geographic context — latitude, altitude, local terrain, proximity to coast — that does not change over time. The DP modules already use lat/lon directly for solar geometry, so this encoder would capture residual structure the hard-coded geometry does not explain.

What the encoders do not need to learn

Because the DP layer hard-codes solar geometry, the weather encoder does not need to learn that "noon → peak irradiance" or "winter → low sun angle." The time encoder does not need to represent seasonality for PV — that is handled by the ephemeris computation in the DP module. The encoders can focus entirely on the residual structure the physics does not explain: NWP biases, local microclimatic effects, and behavioural anomalies.