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name: scienceclaw-benchmark-for33 description: "Use when forecasting day-ahead hourly electricity load of individual buildings (residential and commercial smart-meter data, BuildingsBench-style) from the previous 168 hours and optionally the building's own earlier history, delivering a 24-hour forecast in kWh per window scored by balanced CVRMSE. Corresponds to FoR33 of the companion ScienceClaw-Eval benchmark." metadata: { "openclaw": { "emoji": "📊" } }

Day-ahead building load forecasting (BuildingsBench)

Task. Input per window: 168 hourly loads in kWh (oldest first), building id, category (residential / commercial), ISO target_start; optionally each building's earlier hourly history (n_buildings, T) and latitude/longitude. Deliverable: float array (n, 24), the next 24 hourly loads in kWh, finite, >= 0, in window order.

Quality. Balanced CVRMSE in percent, lower is better: per building 100 * RMSE / mean(y) over all its target hours, median within each category, mean of the two medians. Locally: scilib.loadforecast.balanced_cvrmse(y_true, y_pred, building_id, category). Reference: yesterday's load repeated.

Library (check scienceclaw_tools(operation=show|weights)):

• scilib.loadforecast: context-only yesterday, day_median, core_forecast(context, category); learned fit(load, history_start, building_id, category) then LoadForecaster.candidates(...) (keys yesterday, mean7, median7, core, ml, ens); forecast_candidates, backtest_history (hold-out inside the histories), pick_lowest, history_windows.
• Zero-shot pretrained, guard with available(): scilib.tsfm.forecast (Chronos-2, assets chronos_2 / chronos_bolt_base; candidates(..., pretrained=True) adds chronos2 and ens_chronos2); scilib.buildingsbench.forecast (Transformer-Gaussian-L trained on simulated Buildings-900K, asset buildingsbench_gaussian_l; needs Box-Cox-normalised loads, calendar, latitude/longitude and building type as wired in the operator below).
• Operators: building_load_day_ahead_context_forecast, building_load_day_ahead_learned_ensemble, building_load_day_ahead_pretrained_transformer, building_load_balanced_cvrmse.

Routes. Baselines: persistence, per-hour median of the last 7 days (strong for residential), core_forecast. Learned: the ens ensemble fitted on the buildings' own history. Choose with backtest_history; keep the simple candidate unless another wins clearly.

Rules.

• Respect causality: use only data observed before each target window and only that building's history. With several windows per building, never use a later window's context to forecast an earlier one.
• Medians over a few buildings are noisy and loads drift months after the history ends; a small backtest edge is weak evidence.
• Keep kWh units and (n, 24) shape; clip at 0; a forecast far above the recent maximum signals a unit error.
• Chronos corpora include public electricity-load data, and BuildingsBench models come from the same project as the data; disclose possible overlap.