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site_id
stringlengths
12
12
site_type
stringclasses
2 values
disco
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10 values
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17
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2024-01-01 00:00:00
2024-12-30 23:00:00
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IND-00000000
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industrial
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Ikeja
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2024-10-20 04:00:00
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Kaduna
KAD-SS-002-FD-010
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Kano
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Kano
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2024-05-28 13:00:00
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Benin
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Ikeja
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Jos
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2024-01-28 19:00:00
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Jos
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2024-08-25 12:00:00
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Jos
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2024-06-09 15:00:00
14.137
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Kaduna
KAD-SS-001-FD-001
2024-12-07 10:00:00
21.723
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industrial
Enugu
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2024-05-17 06:00:00
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403.1
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Eko
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2024-02-27 08:00:00
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Eko
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Eko
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2024-03-18 06:00:00
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Abuja
ABJ-SS-001-FD-030
2024-01-05 12:00:00
28.057
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Ikeja
IKJ-SS-002-FD-021
2024-02-05 17:00:00
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Ibadan
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2024-06-19 18:00:00
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IND-00000078
industrial
Enugu
ENG-SS-001-FD-071
2024-10-20 20:00:00
411.969
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14.758
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Abuja
ABJ-SS-003-FD-034
2024-03-23 15:00:00
425.151
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56.676
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Ikeja
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2024-03-31 23:00:00
444.993
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Ikeja
IKJ-SS-003-FD-003
2024-06-22 01:00:00
331.992
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IBD-SS-001-FD-078
2024-09-29 08:00:00
77.088
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Ikeja
IKJ-SS-003-FD-110
2024-10-14 11:00:00
446.664
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Port Harcourt
PHC-SS-001-FD-010
2024-02-28 19:00:00
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End of preview. Expand in Data Studio

Nigerian Energy & Utilities – Commercial & Industrial Consumption | Africa (Electric Sheep Africa metadata inventory)

Size category: 100K<n<1M - Formats: parquet - Sector: energy - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: # Nigerian Energy & Utilities – Commercial & Industrial Consumption 3-phase site hourly consumption with PF, reactive power, and current derived from P, V, PF. - [category] Smart Meters & Consumption - [rows] ~220,000 - [formats] CSV + Parquet (snappy) - [geography] Nigeria (DisCos, substations, plants) ## Schema | column | dtype | |---|---| | site_id | object | | site_type | object | | disco | object | | feeder_id | object | | timestamp | object | | active_power_kw |… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/nigerian_energy_and_utilities_commercial_industrial_consumption.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/nigerian_energy_and_utilities_commercial_industrial_consumption
Sector energy
Topic tags nigeria, energy, utilities, power, grid, smart-meter, renewables
Modalities tabular, text
Formats parquet
Size category 100K<n<1M
Countries Nigeria
ISO3 coverage NGA
Last modified on HF 2025-10-11 18:12:24+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/nigerian_energy_and_utilities_commercial_industrial_consumption")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_nigerian_energy_and_utilities_commercial_industrial_consumption_2026,
  title        = {Nigerian Energy & Utilities – Commercial & Industrial Consumption | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/nigerian_energy_and_utilities_commercial_industrial_consumption},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/nigerian_energy_and_utilities_commercial_industrial_consumption}}
}

License

Released under gpl.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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