targetfev / AutoGluon ↗Solar Photovoltaic Power Output with Weather
Hourly single-asset solar photovoltaic generation benchmark spanning 49,648 observations paired with meteorological covariates for 24-hour day-ahead renewable power forecasting.
Business Objective: Solar Photovoltaic Power Output with Weather
01Business Context
Independent power producers (IPPs), transmission system operators (TSOs), and utility trading desks must submit firm day-ahead generation schedules to wholesale electricity markets (e.g. EPEX SPOT, CAISO, PJM) every 24 hours.
02Analytical Objective
Forecast hourly solar power generation (target) across a rolling 24-hour horizon (H=24) using historical solar telemetry and known numerical weather predictions (NWP).
03Economic & Decision Impact
Over-forecasting incurs severe balancing settlement penalties, real-time dispatch curtailment, and market imbalance fees; under-forecasting forces grid dispatchers to commit costly thermal peaker plants or forgo peak power prices during sunny intervals.
Classical univariate statistical methods (AutoARIMA, Seasonal Naive) capture nighttime zero-clipping but fail during intermittent cloud dynamics. Gradient boosted trees (LightGBM with tabular lag features) excel when ingesting NWP weather covariates, while zero-shot pretrained foundation models (e.g. Mistral's Timestral, TimesFM, MOIRAI) demonstrate strong in-context adaptation to irradiance curves without requiring per-asset gradient fine-tuning.
Target & Feature Column Definitions
Exhaustive business definitions, measurement units, roles, and target variables across all 12 columns.
| Col | Variable / Feature Name | Role | Type | Unit / Scale | Description & Business Meaning |
|---|---|---|---|---|---|
| C | targetSolar Power Output | TARGET | NUM | MW | Actual active solar power generation measured at the photovoltaic inverter |
| A | idSeries Identifier | SERIES ID | CAT | — | Unique categorical identifier indexing the photovoltaic asset or tracking installation |
| B | timestampObservation Timestamp | TIME | TIME | — | ISO 8601 hourly timestamp indicating the start of each observation interval |
| D | day_lengthAstronomical Day Length | Feature | NUM | minutes | Total daylight duration calculated for the geographic coordinates |
| E | humidityRelative Humidity | Feature | NUM | % | Ambient relative humidity percentage measured or forecast at surface level |
| F | pressureAtmospheric Pressure | Feature | NUM | hPa | Barometric surface pressure influencing air density and cloud formation |
| G | rain_1hHourly Rainfall | Feature | NUM | mm | Liquid precipitation accumulated over the preceding 1-hour interval |
| H | snow_1hHourly Snowfall | Feature | NUM | mm | Snowfall depth or liquid equivalent accumulated over the preceding 1-hour interval |
| I | tempAmbient Temperature | Feature | NUM | deg C | Outside air temperature affecting PV panel semiconductor operating efficiency |
| J | wind_speedSurface Wind Speed | Feature | NUM | m/s | Horizontal wind speed contributing to convective cooling of solar panels |
| K | clouds_allTotal Cloud Cover | Feature | NUM | % | Observed percentage of total cloud cover attenuating direct solar irradiance |
| L | global_horizontal_irradianceGlobal Horizontal Irradiance | Feature | NUM | W/m2 | Total solar radiation incident on a horizontal surface at ground level |
Interactive Data Table
Explore rows, feature values, and target labels for Solar Photovoltaic Power Output with Weather.
Foundation Models vs Traditional ML & Baselines (41 Methods)
Empirical evaluation directly from the AutoGluon fev benchmark suite evaluating zero-shot foundation models against deep learning, gradient-boosted trees, and statistical baselines across 100 evaluation tasks.
| Rank | Method / Model | Family | Organization | SQL Win Rate?Average win rate (%) against all competing models across the benchmark tasks under Scaled Quantile Loss. | SQL Skill?Normalized skill score relative to the Seasonal Naive baseline (0% = baseline, higher is better). | MASE Win Rate?Average win rate (%) under Mean Absolute Scaled Error. | Latency?Median end-to-end execution time per 100 time series in seconds. | Link |
|---|---|---|---|---|---|---|---|---|
| #1 | Timestral-209M | Foundation Model | Mistral AI | 87.7% | +50.4% | 86.0% | 0.51s | Repo ↗ |
| #2 | Timestral-123M (Carla Engine) | Foundation Model | Mistral AI | 86.8% | +50.2% | 85.0% | 0.32s | Repo ↗ |
| #3 | TimeRSI | Closed-Source API | GaTech & SamayLabs | 84.8% | +51.2% | 83.8% | 2512s | Repo ↗ |
| #4 | t0-gamma | Foundation Model | The Forecasting Company | 84.4% | +49.4% | 81.8% | 4.38s | Repo ↗ |
| #5 | Timestral-28M | Foundation Model | Mistral AI | 79.2% | +48.4% | 77.0% | 0.11s | Repo ↗ |
| #6 | timesfm-3 | Foundation Model | 78.5% | +49.0% | 77.0% | 3.41s | Repo ↗ | |
| #7 | ChakraTS | Baseline | Community | 78.2% | +48.1% | 79.5% | 14.22s | Repo ↗ |
| #8 | Lingjiang2.0 | Baseline | Community | 73.9% | +47.0% | 71.3% | 8.89s | Repo ↗ |
| #9 | Chronos-2 | Foundation Model | AWS | 70.4% | +47.3% | 68.4% | 0.84s | Repo ↗ |
| #10 | timeh2o-v1-tiny | Foundation Model | H2O.ai | 69.9% | +45.5% | 67.0% | 0.19s | Repo ↗ |
| #11 | Timestral-5M | Foundation Model | Mistral AI | 69.0% | +46.2% | 67.7% | 40ms | Repo ↗ |
| #12 | Toto-2.0-2.5B | Baseline | Community | 67.5% | +44.4% | 65.7% | 5.58s | Repo ↗ |
| #13 | t0-beta | Foundation Model | The Forecasting Company | 67.4% | +46.6% | 64.5% | 2.29s | Repo ↗ |
| #14 | Toto-2.0-1B | Baseline | Community | 66.4% | +44.4% | 65.5% | 2.48s | Repo ↗ |
| #15 | TiRex-2 | Foundation Model | NX-AI | 65.8% | +45.5% | 63.4% | 0.27s | Repo ↗ |
| #16 | Toto-2.0-313m | Baseline | Community | 65.1% | +44.2% | 63.5% | 1.06s | Repo ↗ |
| #17 | TiRex | Foundation Model | NX-AI | 58.0% | +42.6% | 54.1% | 0.24s | Repo ↗ |
| #18 | Toto-2.0-22m | Baseline | Community | 57.6% | +42.9% | 56.0% | 0.25s | Repo ↗ |
| #19 | TimesFM-2.5 | Baseline | Community | 54.6% | +42.2% | 53.5% | 1.87s | Repo ↗ |
| #20 | TabPFN-TS-3 | Foundation Model | Prior Labs | 52.4% | +43.1% | 51.3% | 235s | Repo ↗ |
| #21 | TS-ICL | Foundation Model | EDF-Lab | 51.2% | +43.1% | 49.8% | 2.49s | Repo ↗ |
| #22 | FlowState | Foundation Model | IBM | 49.3% | +39.9% | 49.9% | 2.29s | Repo ↗ |
| #23 | citras-fm | Foundation Model | Hitachi | 48.8% | +41.2% | 46.9% | 0.60s | Repo ↗ |
| #24 | Toto-1.0 | Baseline | Community | 47.5% | +40.8% | 45.9% | 22.35s | Repo ↗ |
| #25 | TabPFN-TS | Foundation Model | Prior Labs | 47.3% | +41.5% | 45.4% | 109s | Repo ↗ |
| #26 | Toto-2.0-4m | Baseline | Community | 46.2% | +40.7% | 43.6% | 0.20s | Repo ↗ |
| #27 | Moirai-2.0 | Baseline | Community | 41.9% | +39.3% | 40.5% | 0.35s | Repo ↗ |
| #28 | Chronos-Bolt | Foundation Model | AWS | 41.4% | +38.9% | 39.3% | 0.26s | Repo ↗ |
| #29 | TFT | Deep Learning | — | 31.7% | +32.2% | 29.2% | 829s | Repo ↗ |
| #30 | Stat. Ensemble | Baseline | Community | 27.5% | +20.7% | 30.6% | 147s | Repo ↗ |
| #31 | Sundial-Base | Foundation Model | Tsinghua University | 27.4% | +33.4% | 33.6% | 8.01s | Repo ↗ |
| #32 | PatchTST | Deep Learning | — | 26.8% | +30.1% | 25.1% | 664s | Repo ↗ |
| #33 | DeepAR | Deep Learning | — | 26.1% | +29.1% | 23.6% | 1113s | Repo ↗ |
| #34 | AutoARIMA | Statistical Baseline | — | 23.3% | +21.3% | 21.5% | 20.14s | Repo ↗ |
| #35 | AutoETS | Statistical Baseline | — | 20.3% | -26.8% | 22.5% | 3.48s | Repo ↗ |
| #36 | CatBoost | Tree Ensemble | — | 19.1% | +23.0% | 35.1% | 29.48s | Repo ↗ |
| #37 | LightGBM | Tree Ensemble | — | 17.8% | +21.0% | 33.1% | 3.03s | Repo ↗ |
| #38 | AutoTheta | Statistical Baseline | — | 15.6% | +5.5% | 19.8% | 3.28s | Repo ↗ |
| #39 | Seasonal Naive | Baseline | Community | 10.3% | 0.0% | 11.8% | 0.48s | Repo ↗ |
| #40 | Naive | Statistical Baseline | — | 6.7% | -45.4% | 10.9% | 0.47s | Repo ↗ |
| #41 | Drift | Statistical Baseline | — | 6.1% | -45.8% | 10.0% | 0.45s | Repo ↗ |
Variable Schema & Summary Distributions
Observed numerical ranges, category cardinalities, missing rates, and sample values across 49,648 rows.
| Col | Variable Name | Type | Missing | Distinct | Summary Stats / Distribution | Sample Values |
|---|---|---|---|---|---|---|
| C | targetTARGET | NUM | 0 | 10,349 | [0, 19008] μ=2271.1 σ=4066.2 | 000 |
| A | id | CAT | 0 | 1 | — | 000 |
| B | timestamp | CAT | 0 | 49,648 | — | 2017-01-01T01:00:002017-01-01T02:00:002017-01-01T03:00:00 |
| D | day_length | NUM | 456 (0.00918%) | 39 | [450, 1020] μ=748.6 σ=194.9 | 450450450 |
| E | humidity | NUM | 456 (0.00918%) | 79 | [22, 100] μ=79.8 σ=15.6 | 100100100 |
| F | pressure | NUM | 456 (0.00918%) | 71 | [977, 1047] μ=1015.3 σ=9.6 | 102010201019 |
| G | rain_1h | NUM | 456 (0.00918%) | 311 | [0, 8.09000015258789] μ=0.1 σ=0.3 | 000 |
| H | snow_1h | NUM | 456 (0.00918%) | 129 | [0, 2.819999933242798] μ=0.0 σ=0.1 | 000 |
| I | temp | NUM | 456 (0.00918%) | 503 | [-16.600000381469727, 35.79999923706055] μ=9.8 σ=8.0 | 1.70000004768371581.8999999761581422 |
| J | wind_speed | NUM | 456 (0.00918%) | 136 | [0, 14.300000190734863] μ=3.9 σ=1.8 | 5.1999998092651375.55.699999809265137 |
| K | clouds_all | NUM | 456 (0.00918%) | 101 | [0, 100] μ=66.0 σ=36.6 | 100100100 |
| L | global_horizontal_irradiance | NUM | 456 (0.00918%) | 8,553 | [0, 227.8249969482422] μ=32.6 σ=51.7 | 000 |
Frequently Asked Questions: Solar Photovoltaic Power Output with Weather
Common questions regarding the Solar Photovoltaic Power Output with Weather dataset, machine learning task formulations, and in-browser tabular inference.
What is the forecasting horizon for Solar Photovoltaic Power Output with Weather?
The canonical horizon is 24 hours (H=24) at an hourly cadence, designed to mirror day-ahead electricity market clearing and transmission scheduling requirements.
Can I run zero-server forecasts on this dataset in Google Sheets?
Yes, Carla enables zero-server in-context time series forecasting directly in spreadsheets using Mistral's Timestral foundation model without moving data to external servers.
Which models perform best on Solar Photovoltaic Power Output with Weather?
Mistral's Timestral zero-shot foundation model and LightGBM with numerical weather covariates outperform classical univariate baselines (AutoARIMA, Seasonal Naive) by capturing non-linear weather dependencies.
Test TabICLv2 on Solar Photovoltaic Power Output with Weather Yourself
Open the pre-loaded Google Sheet and let Carla configure the target and task for local, zero-cloud tabular machine learning.
Launch Carla
Click once to open the spreadsheet and Carla side panel together.
Review the Setup
Carla selects the dataset target and task from this page automatically.
Evaluate & Predict
Run predictions and compute metrics with zero server uploads.
Provenance & Attribution
NREL Solar Power and Meteorological Telemetry Benchmark.