--- pretty_name: PowerZoo Dataset license: other license_name: mixed-upstream-licences language: - en size_categories: - 1M.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. ---