gridload-compact

A compact probabilistic load forecaster for the two SMARD net-load challenges of TS-Arena (24 h at 15 min, 72 h at 1 h): two small MLP quantile nets, 160,278 parameters in total, 144 kB and 150 kB as int8 ONNX, about 0.4 to 1.2 ms per series on one CPU thread. Self-evaluated on the TS-Arena Archive; not an official ranking. Code, protocol, ledger of the evaluations and reproduction scripts: https://github.com/sankalpsthakur/gridload-compact

Model description

• Two MLP quantile nets, one per challenge (15 min x 96 steps, 1 h x 72 steps). Each net is a context encoder plus a shared per-step head (hidden width 64) that emits nine monotone deciles per step (0.1 to 0.9); the point forecast is the median.
• 3-seed ensembles per frequency; the quantile curves are averaged inside one ONNX graph. Parameters: 25,817 per net at 15 min and 27,609 hourly; 77,451 and 82,827 per ensemble; 160,278 in total.
• A post-hoc scalar on the decile spread (1.05 at 15 min, 1.20 hourly) was chosen on validation data only.
• ONNX, fp32 (344,362 B and 365,869 B) and int8 by dynamic quantization (144,175 B and 149,554 B). The service default is int8.
• Inference: a numpy feature pipeline (no torch) and ONNX Runtime on one thread. A clock-signature gate compares the daily profile of the context with a fixed reference profile (weights/clock_ref.npz); anything that is not a SMARD net-load series on the platform clock goes to a generic seasonal fallback with residual deciles, for which no claim is made.
• Public holiday calendars (DE federal and state, AT) are baked into weights/hol_table.npz and derived from timestamps only.

Intended use and limits

• Intended use: probabilistic net-load forecasts for the ten SMARD net-load series of the two TS-Arena challenges, on the platform's timestamp convention (UTC stamps that run 1 to 2 h early; the hourly series is the mean of the four quarter-hours), from a context of 1,000 points; CPU-only and edge-sized deployments; benchmarking and research.
• Not covered: other series, frequencies or horizons are served by the seasonal fallback and no claim is made for them. Its held-out evaluation covers only archive rounds of 2026-02-08 .. 2026-03-31; it has no live or operational track record and is not validated for grid operation or trading.
• The comparison with foundation models is a specialist trained on the same SMARD series (cutoff before the window) against zero-shot generalists that saw none of it: the fair reading is "a specialist this small is competitive or better on its own series", not "better architecture".
• Training is not bit-reproducible across hardware; the SHA-256 of the checkpoints behind the numbers are in weights/net15_info.json, weights/net60_info.json and configs/final.json of the GitHub repository.

Training data and cutoff

• SMARD net load (Bundesnetzagentur | SMARD.de, CC BY 4.0), revised history from 2018-01 fetched from the public API. The shipped weights are the test-stage ensembles, trained on targets up to 2026-02-06 23:45; the architecture selection used models cut at 2025-06-30 23:45 and the spread scalar used models cut at 2025-12-31 23:45. The first evaluation round registers on 2026-02-08.
• Public holiday table generated with the python holidays package (MIT), version 0.106.
• No third-party model weights, and nothing from the TS-Arena Archive, were used for training or calibration.

Evaluation

Self-evaluated on the TS-Arena Archive, window TEST-F (52 rounds, 2026-02-08 .. 2026-03-31; training cutoff 2026-02-06 23:45; configuration frozen and each variant evaluated once, pre-registered). Headline variant = the service code path with the int8 weights, scored with the platform's arena MASE and SQL against the 32 reference models of the Archive's field:

• Definition 2 (24 h at 15 min): lower mean MASE than every reference model on the full window. MASE 0.217 [0.173, 0.273] against 0.251 [0.184, 0.327] for the best reference model of the window, visiontspp-large (307 M parameters, about 4,000 times larger); per-round difference -0.034 [-0.059, -0.009]; SQL 0.173 against 0.201, difference -0.028 [-0.046, -0.011]. Caveat that belongs to the claim: the interval excludes 0 over the full window, but over the first 35 rounds (02-08 .. 03-14, which the live board mostly reproduces; 03-06 .. 03-08 are partly re-scored there) it does not (-0.012 [-0.026, +0.006]); the difference is carried by the 17 archive-only late-March rounds (-0.070 [-0.111, -0.021] against that window's best reference, flowstate).
• Definition 5 (72 h at 1 h): a tie, not a win. MASE 0.185 [0.161, 0.214] against 0.187 [0.157, 0.226] for chronos-2 (120 M parameters, about 1,400 times larger); difference -0.002 [-0.015, +0.007]; SQL 0.148 against 0.150, difference -0.002 [-0.014, +0.006]. The model matches the best reference at a fraction of its size; no win is claimed.

Scoring is the platform's arena MASE (MAE of the forecast divided by the MAE of the last-context-value persistence forecast over the same horizon) and SQL (scaled quantile loss, same scale), reimplemented from the MIT backend of TS-Arena. This is a self-obtained evaluation on the TS-Arena Archive and not an official ranking. Protocol: selection rule, ensemble, spread grid and the eight evaluated variants were pre-registered before any sweep, validation-stage or test-stage number existed; the configuration was frozen (hashes of the test-stage checkpoints) before the TEST-F evaluation; each variant was evaluated once (16 evaluations), and an unchanged re-run of the frozen kernel gives byte-identical evidence tables. The definition-2 difference is carried by 17 archive-only late-March rounds; the intervals are the platform's ISO-week block bootstrap (9 blocks in the full window) and are crude. Full tables, sub-windows, the ledger of all evaluations and the integrity checklist are in the GitHub repository (TEST-LOG.md, INTEGRITY.md, results/).

Size and latency

	15 min (definition 2)	hourly (definition 5)
parameters per net / ensemble of 3	25,817 / 77,451	27,609 / 82,827
ONNX ensemble, fp32 / int8	344,362 B / 144,175 B	365,869 B / 149,554 B
spread (post-hoc scalar on the deciles, chosen on VAL-F)	1.05	1.20
latency, int8, model only, p50 (Kaggle CPU, AMD EPYC 7B12, 1 thread, batch 1, n=300)	0.27 ms	0.23 ms
latency, int8, end to end per series (numpy features + model), p50	1.16 ms	1.23 ms
latency, fp32, model only / end to end, p50	0.28 ms / 1.18 ms	0.24 ms / 1.27 ms
latency, Apple M4 (macOS), 1 thread, batch 1, int8, model only / end to end, p50	0.20 ms / 0.44 ms	0.16 ms / 0.44 ms
latency, Apple M4 (macOS), fp32, model only / end to end, p50	0.14 ms / 0.39 ms	0.12 ms / 0.39 ms

Sources: results/bench-timeseries-b/out/bench_kaggle_cpu_{15,60}.json and results/bench-timeseries-b/m4_latency_{15,60}.json in the GitHub repository (median of 300 calls after warm-up, numpy features included in the end-to-end figure).

How to run the service

The service (FastAPI, POST /predict, GET /health) is in the GitHub repository under submission/model-services/gridload-compact:

git clone https://github.com/sankalpsthakur/gridload-compact
cd gridload-compact/submission/model-services/gridload-compact
pip install -r requirements.txt            # fastapi, uvicorn, pydantic, numpy, onnxruntime (pinned)
uvicorn app.main:app --port 8000           # reads ./weights, the same files as this repository

To use the files of this Hugging Face repository instead (run both from the service directory):

from huggingface_hub import snapshot_download
snapshot_download("sankalpsthakur/gridload-compact", local_dir="hf-weights")

GRIDLOAD_WEIGHTS=hf-weights uvicorn app.main:app --port 8000      # GRIDLOAD_PRECISION=fp32 selects the fp32 graphs (default int8)

Request: {"history": [{"ts": "2026-03-10T07:45:00.000Z", "value": 12345.0}, ...], "horizon": 96, "freq": "15min"} ("freq": "h" with "horizon": 72 for the hourly challenge; a list of such lists is a batch). Response: {"prediction": [{"ts": ..., "value": ..., "probabilistic_values": {"q_0.1": ..., ..., "q_0.9": ...}}, ...]}.

Files

file	what
weights/net15.onnx, weights/net15_int8.onnx	15 min x 96 steps, 3-seed ensemble, fp32 (344,362 B) and int8 (144,175 B)
weights/net60.onnx, weights/net60_int8.onnx	1 h x 72 steps, 3-seed ensemble, fp32 (365,869 B) and int8 (149,554 B)
weights/net15_info.json, weights/net60_info.json	feature specification, spread, parameter counts, SHA-256 of the member checkpoints and of the ONNX files
weights/hol_table.npz	baked public holiday calendar table (DE federal and state, AT)
weights/clock_ref.npz	reference daily profile of the clock gate
README.md	this card

Licences

• Code and model weights: Apache-2.0.
• Training data: SMARD, Bundesnetzagentur | SMARD.de, CC BY 4.0 (https://www.smard.de/); the weights are derived from it and changes were made.
• Evaluation data: TS-Arena-Archive (DAG-UPB/TS-Arena-Archive), CC BY 4.0; used for evaluation only, not contained in the weights.
• Holiday table: generated with the python holidays package (MIT).

Self-evaluated, not peer-reviewed. Sankalp Thakur, October 2026.