Spaces:
Running
Running
| title: GridPulse | |
| emoji: ⚡ | |
| colorFrom: green | |
| colorTo: purple | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: true | |
| license: mit | |
| short_description: Day-ahead grid demand forecasting benchmarked vs the EIA | |
| # GridPulse | |
| Day-ahead electricity demand forecasting for US balancing authorities, | |
| benchmarked against the EIA's own published day-ahead forecast. | |
| <!-- RESULTS:START --> | |
| LightGBM hybrid (+ EIA forecast as input) gets 2.877% MAPE where the EIA's own published forecast gets 3.655%, which is 21.3% better, measured on 25,918 test hours from 2026-05-17 onwards across 12 balancing authorities. | |
| | Model | MAPE % | MAE (MW) | RMSE (MW) | R2 | Peak-hour MAPE % | Hours scored | Skill vs EIA | | |
| |---|---|---|---|---|---|---|---| | |
| | **LightGBM hybrid** (+ EIA forecast as input) | 2.877 | 1,136 | 2,002 | 0.9959 | 3.435 | 25,918 | **+21.3%** | | |
| | **LightGBM** (global, quantile) | 3.644 | 1,430 | 2,372 | 0.9943 | 4.657 | 25,918 | **+0.3%** | | |
| | _EIA official forecast_ | 3.655 | 1,405 | 2,487 | 0.9938 | 2.848 | 25,342 | - (benchmark) | | |
| | **Ensemble** (GBM + LSTM) | 4.490 | 1,694 | 2,688 | 0.9927 | 3.622 | 25,918 | -22.9% | | |
| | Seasonal naive (24h) | 5.572 | 1,942 | 3,206 | 0.9896 | 5.271 | 25,918 | -52.5% | | |
| | **LSTM** encoder | 5.874 | 2,136 | 3,481 | 0.9877 | 2.997 | 25,914 | -60.7% | | |
| | **Transformer** encoder | 8.328 | 3,301 | 4,913 | 0.9755 | 6.073 | 25,914 | -127.8% | | |
| | Weekly naive (168h) | 9.460 | 3,566 | 6,072 | 0.9625 | 13.093 | 25,918 | -158.8% | | |
| The P10, P50 and P90 rows are left out of this table. They draw the prediction interval rather than competing as point forecasts. | |
| <!-- RESULTS:END --> | |
| ## What you can do here | |
| | Tab | What it does | | |
| |---|---| | |
| | **Forecast** | Make a live 24-hour forecast with a P10-P90 range for any of the 12 regions, using the current weather forecast | | |
| | **Explorer** | Past demand, the V-shaped link between temperature and demand, and how weekdays differ from weekends | | |
| | **Model Leaderboard** | Every model scored against EIA's own forecast on exactly the same test hours | | |
| | **Anomalies** | Hours that look wrong, found by three detectors that have to agree | | |
| | **Data Quality** | A scorecard covering six categories of data quality | | |
| | **Ask the Grid** | Ask a question in normal English and get SQL and a chart back | | |
| ## What is behind it | |
| This Space is just the website. Behind it there is a full data pipeline: | |
| - Downloads data from the EIA and Open-Meteo APIs a bit at a time, remembering | |
| where it got to so it never re-downloads the same thing | |
| - Stores it in bronze, silver and gold layers, ending in a star schema in DuckDB | |
| - Runs 16 data quality checks covering completeness, validity, consistency, | |
| timeliness, duplicates and accuracy | |
| - Trains LightGBM with P10/P50/P90 bands, plus an LSTM and a Transformer in | |
| PyTorch so I could compare them properly | |
| - Finds unusual hours using three different detectors that have to agree: a | |
| seasonal z-score, an Isolation Forest, and an autoencoder trained on daily | |
| demand shapes | |
| - Has an LLM that writes SQL, wrapped in guardrails (read-only connection, SELECT | |
| only, a list of allowed tables, and a row limit) | |
| ## Setup | |
| The Ask the Grid tab needs a `GROQ_API_KEY` under | |
| Settings, Variables and secrets. Keys are free at | |
| [console.groq.com/keys](https://console.groq.com/keys). Everything else works with | |
| no setup at all. | |
| ## Source | |
| Full pipeline, tests, orchestration and documentation: | |
| [github.com/adwitiyashukla/gridpulse](https://github.com/adwitiyashukla/gridpulse) | |
| Data: [US EIA Form 930](https://www.eia.gov/opendata/) and | |
| [Open-Meteo](https://open-meteo.com/). | |