EnergyDecision-DT-V2-Forecast
⚠️ NEGATIVE RESULT — Superseded by the Stage C standalone DT.
This ForecastDecisionTransformer (48-step TTM forecast tokens + modern v2 backbone) scored $4,564/ep on the standard tier — 8.5% below the modern v2 baseline ($4,991/ep). Explicit TTM price forecasts add no meaningful edge over the implicit 210-step context window.
The shipped model is now the Stage C standalone DT distilled from an honest SDP-planning teacher, which beats PPO on all 4 identity surfaces and passes the impact gate without any explicit forecast tokens:
Surface Stage C DT Forecast DT PPO Standard Oct $11,573 $4,564 $2,353 Dispatch-matched $35,320 — $22,530 This checkpoint is retained as a documented negative result. See report.md §8.2.8 for the full analysis.
Model Description
EnergyDecision-DT-V2-Forecast is a ForecastDecisionTransformer — an extension of the modern v2 Decision Transformer that conditions on 48-step TTM price forecasts alongside the historical observation/action sequence. It predicts optimal battery dispatch and FCAS bids using both past market data and predicted future prices (RRP, demand, 4 FCAS services).
Key Features
- Forecast token prefix: 48 TTM-generated price forecasts prepended as a learned prefix
- RoPE enabled: Rotary Position Embeddings for position-aware sequence modeling
- Modern architecture: 8×768 GQA, QK-Norm, SwiGLU, RMSNorm, weight tying
- Action Space (9-dim): Energy dispatch + 8 FCAS contingency bids
- Context Length: 210 history + 48 forecast = 258 timesteps total
Why This Didn't Work
The TTM forecasts have near-zero correlation with FCAS prices (~0.01–0.07). The model simply learns to ignore the forecast tokens and rely on history alone — the same information the standard DT already has.
Usage
from forecast_decision_transformer import ForecastDecisionTransformer
model = ForecastDecisionTransformer(
state_dim=18, act_dim=9, n_block=8, h_dim=768,
context_len=210, forecast_len=48, n_heads=12,
drop_p=0.15, max_timestep=100000,
rope_enabled=True, n_kv_heads=6, qk_norm=True, tie_weights=True,
)