| --- |
| pretty_name: PowerZoo Dataset |
| license: other |
| license_name: mixed-upstream-licences |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - power-systems |
| - electricity |
| - reinforcement-learning |
| - benchmark |
| - time-series |
| - smart-grid |
| - data-center |
| - load-forecasting |
| - opf |
| task_categories: |
| - time-series-forecasting |
| - tabular-regression |
| - reinforcement-learning |
| configs: |
| - config_name: aemo_5min_demand |
| data_files: parquet/AEMO_5min_Demand_2025_2026.parquet |
| - config_name: aemo_forecast |
| data_files: parquet/AEMO_Forecast_vs_Actual_2025.parquet |
| - config_name: ausgrid_zone_substation |
| data_files: parquet/Ausgrid_Zone_Substation_FY25_imputed_15min.parquet |
| - config_name: gb_forecast_actual_demand |
| data_files: parquet/GB_Forecast_Actual_Demand_2023_2025_30min.parquet |
| - config_name: gb_gen_by_type |
| data_files: parquet/GB_Gen_by_Type_2016_2025_30min.parquet |
| - config_name: gb_neso_demand |
| data_files: parquet/GB_NESO_Demand_2009_2025_30min.parquet |
| - config_name: gb_market_mid |
| data_files: parquet/MID_GB_30min_aligned_to_gen.parquet |
| - config_name: alibaba_dc_2018 |
| data_files: parquet/alibaba_dc_2018_300s.parquet |
| - config_name: alibaba_gpu_2020 |
| data_files: parquet/alibaba_gpu_2020_300s.parquet |
| - config_name: azure_dc_v2 |
| data_files: parquet/azure_dc_v2_300s.parquet |
| - config_name: google_dc_2019 |
| data_files: parquet/google_dc_2019_300s.parquet |
| --- |
| |
| # PowerZoo Dataset |
|
|
| Real-world power-system and data-centre time-series, canonical grid topologies, and JSON manifests linking the two — packaged for reinforcement-learning and forecasting research with the PowerZoo / PowerZooJax benchmark code (released separately). |
|
|
| --- |
|
|
| ## 1. What's inside |
|
|
| 1. Eleven parquet time-series files ingested from public regulator and cloud-provider releases (GB / AU electricity load, generation by fuel, day-ahead forecasts, market mid-prices; Alibaba / Azure / Google data-centre utilisation). |
| 2. Fourteen electrical-network case files (Python classes with bus / branch / generator / load tables) covering transmission systems from 5 to 2383 buses and distribution systems from 33 to 533 buses. |
| 3. Eleven JSON manifests that map each parquet's raw columns to a shared canonical schema (e.g. `OPERATIONAL_DEMAND` → `load.actual_mw`), so traces from different sources compose cleanly in one experiment. |
|
|
| --- |
|
|
| ## 2. Repository layout |
|
|
| ``` |
| PowerZooDataset/ |
| ├── README.md # this file |
| ├── parquet/ # harmonised time-series traces |
| │ ├── *.parquet # primary data files |
| │ └── *.json # per-file provenance metadata (rows, dtypes, source URL, generation timestamp) |
| ├── manifests/ # loader manifests (column maps, derivations, normalisation) |
| │ └── <dataset_id>.json |
| └── powergrid_case/ # electrical-network case definitions (Python) |
| ├── CaseBase.py # ClearCase base class + ext. DataFrame |
| ├── transmission/ # HV / sub-transmission test systems |
| │ ├── Case5.py, Case14.py, Case29GB.py, Case118.py, |
| │ ├── Case300.py, Case552GB.py, Case1354pegase.py, Case2383wp.py |
| │ └── __init__.py |
| └── distribution/ # MV distribution test feeders |
| ├── Case33bw.py, Case118zh.py, Case123.py, Case141.py, |
| ├── Case533mt_hi.py, Case533mt_lo.py |
| └── __init__.py |
| ``` |
|
|
| ### 2.1 Parquet traces |
|
|
|
|
| | File | Domain | Resolution | Rows | Columns | Bytes | |
| | ---------------------------------------------------- | --------------------------------------------- | ---------- | --------- | ------- | ------- | |
| | `AEMO_5min_Demand_2025_2026.parquet` | AU NEM demand (5 regions) | 5 min | 737,400 | 5 | 6.5 MB | |
| | `AEMO_Forecast_vs_Actual_2025.parquet` | AU NEM probabilistic forecast vs. actual | 30 min | 89,145 | 10 | 1.6 MB | |
| | `Ausgrid_Zone_Substation_FY25_imputed_15min.parquet` | NSW zone substations (175 sites) | 15 min | 6,095,040 | 4 | 60 MB | |
| | `GB_NESO_Demand_2009_2025_30min.parquet` | GB NESO historical demand | 30 min | 285,454 | 22 | 4.7 MB | |
| | `GB_Forecast_Actual_Demand_2023_2025_30min.parquet` | GB day-ahead forecast vs. actual | 30 min | 48,283 | 3 | 0.8 MB | |
| | `GB_Gen_by_Type_2016_2025_30min.parquet` | GB generation by fuel type | 30 min | 180,048 | 13 | 6.2 MB | |
| | `MID_GB_30min_aligned_to_gen.parquet` | GB APX/N2EX mid prices & volumes | 30 min | 48,283 | 6 | 0.8 MB | |
| | `alibaba_dc_2018_300s.parquet` | Alibaba production cluster (CPU/mem/net/disk) | 5 min | 2,243 | 6 | 0.1 MB | |
| | `alibaba_gpu_2020_300s.parquet` | Alibaba GPU cluster (GPU/CPU util) | 5 min | 415 | 3 | <0.1 MB | |
| | `azure_dc_v2_300s.parquet` | Azure VM trace v2 (CPU, assigned memory) | 5 min | 8,640 | 3 | 0.2 MB | |
| | `google_dc_2019_300s.parquet` | Google Borg 2019 (CPU/mem/CPI) | 5 min | 8,064 | 5 | 0.3 MB | |
|
|
|
|
| Each `*.parquet` ships with a sibling `*.json` capturing source URL, source organisation, generation timestamp, exact column dtypes, region/category enumerations and (where applicable) timezone conventions. |
|
|
| ### 2.2 Manifests |
|
|
| Manifests in `manifests/` are the contract used by PowerZoo / PowerZooJax loaders. A manifest declares: |
|
|
| - `parquet_file` and the matching `metadata_json` |
| - `column_map`: rename rules from raw column → canonical schema (e.g. `OPERATIONAL_DEMAND → load.actual_mw`) |
| - `index_map`: which raw columns serve as `datetime` / `region` / `issue_time` / `target_time` |
| - `derived`: closed-form derivations from raw columns (e.g. `wind.available_mw = "Wind Offshore + Wind Onshore"`) |
| - `normalize`: per-channel scaling factors applied at load time |
| - `time_mode`: `calendar` (absolute UTC) or `profile` (cyclical, anchored to `data_epoch`) |
| - `region_values`, `date_range`, `source_url`, `source_organization` |
|
|
| The 11 manifests cover every parquet file shipped here. Note that `gb_neso_demand` uses a two-column index (`SETTLEMENT_DATE` + `SETTLEMENT_PERIOD` 1–50) instead of a single `datetime` column — the manifest's `datetime_recipe` field documents the exact reconstruction (Europe/London tz-localise then convert to UTC; SP=49–50 absorb the autumn clock-change repeat). |
|
|
| ### 2.3 Power-grid case files |
|
|
| `powergrid_case/` contains a unified Python representation for both transmission and distribution test systems. Every case subclasses `ClearCase` and exposes four `pandas.DataFrame` tables in MATPOWER-compatible units (MW, MVAr, p.u.): |
|
|
| - `nodes` — `id, type (1=PQ / 2=PV / 3=Ref), Pd, Qd, x, y` |
| - `units` — `id, bus_id, mc_a, mc_b, mc_c, p_max, p_min` (quadratic cost + capacity) |
| - `lines` — `id, from, to, x, floor, cap` (reactance + thermal limits) |
| - `loads` — `id, bus_id, mc_a, mc_b, mc_c, d_max, d_min` (price-responsive demand) |
|
|
| Each file declares `BUS_COUNT`, `VOLTAGE_LEVEL`, `SOURCE` and `DESCRIPTION` as class-level metadata. The values below are reproduced verbatim from those declarations (so the file itself is the source of truth): |
|
|
|
|
| | File | Voltage | Buses | `SOURCE` | `DESCRIPTION` | |
| | -------------------------------- | ------- | ----- | -------- | ------------------------------------------------------------------ | |
| | `transmission/Case5.py` | HV | 5 | MATPOWER | IEEE 5-bus test system | |
| | `transmission/Case14.py` | HV | 14 | MATPOWER | IEEE 14-bus test system | |
| | `transmission/Case29GB.py` | HV | 29 | custom | GB reduced 29-bus transmission network | |
| | `transmission/Case118.py` | HV | 118 | MATPOWER | IEEE 118-bus test system | |
| | `transmission/Case300.py` | HV | 300 | MATPOWER | IEEE 300-bus test system | |
| | `transmission/Case552GB.py` | HV | 552 | GB | Great Britain 552-bus transmission (distinct from 29-bus Case29GB) | |
| | `transmission/Case1354pegase.py` | HV | 1354 | MATPOWER | European PEGASE 1354-bus system | |
| | `transmission/Case2383wp.py` | HV | 2383 | MATPOWER | Polish 2383-bus winter peak system | |
| | `distribution/Case33bw.py` | MV | 33 | MATPOWER | IEEE 33-bus Baran & Wu radial distribution | |
| | `distribution/Case118zh.py` | MV | 118 | MATPOWER | 118-bus Zhang distribution system | |
| | `distribution/Case123.py` | MV | 123 | MATPOWER | IEEE 123-bus three-phase distribution | |
| | `distribution/Case141.py` | MV | 141 | MATPOWER | 141-bus Caracas distribution system (Khodr et al., EPSR 2008) | |
| | `distribution/Case533mt_hi.py` | MV | 533 | MATPOWER | 533-bus Swedish distribution (high load) | |
| | `distribution/Case533mt_lo.py` | MV | 533 | MATPOWER | 533-bus Swedish distribution (low load) | |
|
|
|
|
| --- |
|
|
| ## 3. Loading |
|
|
| ### 3.1 Direct parquet load (no extra dependency on PowerZoo) |
|
|
| ```python |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download( |
| repo_id="PowerZooJax/PowerZooDataset", |
| repo_type="dataset", |
| filename="parquet/AEMO_5min_Demand_2025_2026.parquet", |
| ) |
| df = pd.read_parquet(path) |
| print(df.head()) |
| ``` |
|
|
| ### 3.2 Via `datasets` (per-config) |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset( |
| "PowerZooJax/PowerZooDataset", |
| name="aemo_5min_demand", |
| split="train", |
| ) |
| ``` |
|
|
| ### 3.3 Via the PowerZooJax `DataLoader` |
|
|
| If you have the (separately released) benchmark package installed, instantiate `DataLoader` with this repository's `parquet/` and `manifests/` directories: |
|
|
| ```python |
| from powerzoojax.data import DataLoader |
| |
| loader = DataLoader( |
| data_dir="/path/to/PowerZooDataset/parquet", |
| manifest_dir="/path/to/PowerZooDataset/manifests", |
| ) |
| print(loader.list_available_datasets()) |
| df = loader.load_actual_series("aemo_5min_demand") |
| ``` |
|
|
| ### 3.4 Power-grid cases |
|
|
| ```python |
| from powerzoo.case.distribution.Case141 import Case141 |
| case = Case141() |
| case.check() |
| case.get_node_ptdf() |
| ``` |
|
|
| --- |
|
|
| ## 4. Schema conventions |
|
|
| - **Time stamps.** Calendar-mode parquet files store timestamps as `datetime64[ns, UTC]`, with the exception of `GB_NESO_Demand_2009_2025_30min.parquet`, which uses two columns (`SETTLEMENT_DATE` + `SETTLEMENT_PERIOD`) — see the `datetime_recipe` in `manifests/gb_neso_demand.json`. The Ausgrid metadata explicitly declares `timezone_local = "Australia/Sydney"` and `timezone_stored = "UTC"`; for the other calendar sources, only the stored UTC representation is documented in metadata. |
| - **Profile-mode traces.** The data-centre traces (Alibaba / Azure / Google) are tagged `time_mode = "profile"` and `cyclical = true` in their manifests, anchored to a `data_epoch` (Alibaba DC: `2018-01-06`, Alibaba GPU: `2020-07-01`, Azure: `2019-01-01`, Google: `2019-05-01`). They are intended as periodic exogenous signals, not absolute calendar series. |
| - **Imputation.** `Ausgrid_Zone_Substation_FY25_imputed_15min.parquet` contains imputed values (as indicated by its filename suffix and source path); the imputation method is not documented inside the metadata shipped here. Users requiring raw observations should pull from the upstream Ausgrid release. |
| - **Units.** Power columns in the parquet traces are in MW. The GB market-mid file uses prices in `mid_price_`* columns (the upstream Elexon convention is £/MWh; consult the source URL for any unit caveats). Data-centre utilisation columns are 0–100 (percent) before the manifest's `normalize` factor is applied. |
|
|
| --- |
|
|
| ## 5. Source acknowledgements & licensing |
|
|
| This repository **redistributes derivative datasets** built from publicly accessible upstream releases. Each upstream is governed by its own terms; users are responsible for complying with them. The `source_url` and `source_organization` fields in every `parquet/*.json` and `manifests/*.json` give the canonical pointer. |
|
|
|
|
| | Trace | Upstream | Original portal | |
| | ------------------------------------- | -------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
| | AEMO 5-min demand & POE forecasts | Australian Energy Market Operator | [https://visualisations.aemo.com.au/aemo/nemweb/](https://visualisations.aemo.com.au/aemo/nemweb/) | |
| | Ausgrid zone-substation load | Ausgrid | [https://www.ausgrid.com.au/Industry/Our-Research/Data-to-share/Distribution-zone-substation-data](https://www.ausgrid.com.au/Industry/Our-Research/Data-to-share/Distribution-zone-substation-data) | |
| | GB demand actual & day-ahead forecast | Elexon (BMRS) | [https://data.elexon.co.uk/bmrs/api/v1](https://data.elexon.co.uk/bmrs/api/v1) | |
| | GB generation by fuel type | Elexon (BMRS) | [https://data.elexon.co.uk/bmrs/api/v1/generation/actual/per-type](https://data.elexon.co.uk/bmrs/api/v1/generation/actual/per-type) | |
| | GB historical demand 2009–2025 | National Energy System Operator (NESO) | [https://www.neso.energy/data-portal/historic-demand-data](https://www.neso.energy/data-portal/historic-demand-data) | |
| | Alibaba cluster trace 2018 | Alibaba Group | [https://github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018](https://github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018) | |
| | Alibaba GPU trace 2020 | Alibaba Group | [https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020](https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020) | |
| | Azure public dataset v2 | Microsoft | [https://github.com/Azure/AzurePublicDataset](https://github.com/Azure/AzurePublicDataset) | |
| | Google cluster data 2019 | Google | [https://github.com/google/cluster-data](https://github.com/google/cluster-data) | |
|
|
|
|
| The packaging artefacts authored for this benchmark (the manifest schema, case-file Python representations, schema harmonisation logic) are intended to be released under a permissive open-source licence at camera-ready time; the underlying parquet data inherits the upstream licence in every case. The canonical copy is the upstream URL above. |
|
|
| All series are aggregated at substation, regional, or national level; no PII. |
|
|
| --- |
|
|
| ## 6. Intended use & limitations |
|
|
| **Intended use.** Reinforcement-learning research, load / generation forecasting, OPF benchmarking, distribution-system control, data-centre power-shaping, demand-response studies. |
|
|
| **Limitations.** |
| - Static snapshot at release time; upstream sources continue to publish. Redownload from the URLs in §5 for live data. |
| - Geographic scope: Great Britain and Australia only. |
| - The Ausgrid trace is imputed by the upstream publisher; the imputation procedure is not documented here. |
| - Data-centre traces ship only the columns each manifest maps, at 5-minute resolution; upstream releases offer more fields at finer cadences. |
| - Grid-case parameter values are consistent with the named source systems but are not byte-identical to any specific upstream release. |
|
|
| Not intended for real-time grid control, retail tariff design, or settlement of real markets. |
|
|
| --- |
|
|
| ## 7. File integrity |
|
|
| To regenerate provenance metadata locally: |
|
|
| ```bash |
| python -c " |
| import pyarrow.parquet as pq, glob, os |
| for p in sorted(glob.glob('parquet/*.parquet')): |
| m = pq.read_metadata(p) |
| print(f'{os.path.basename(p):60s} rows={m.num_rows:>10,} cols={m.num_columns:>3} bytes={os.path.getsize(p):>10,}') |
| " |
| ``` |
|
|
| Expected output is reproduced in §2.1. |
|
|
| --- |
|
|
