Datasets:
metadata
license: openrail
size_categories:
- 1K<n<10K
task_categories:
- time-series-forecasting
dataset_info:
features:
- name: start
dtype: timestamp[us]
- name: target
sequence: float64
- name: feat_static_cat
sequence: int64
- name: feat_dynamic_real
dtype: 'null'
- name: item_id
dtype: string
splits:
- name: train
num_bytes: 77588260
num_examples: 370
- name: validation
num_bytes: 77730340
num_examples: 370
- name: test
num_bytes: 77872420
num_examples: 370
download_size: 123583004
dataset_size: 233191020
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
Download the Dataset:
from datasets import load_dataset
dataset = load_dataset("LeoTungAnh/electricity_hourly")
Dataset Card for Electricity Consumption
This dataset encompasses hourly electricity consumption in kilowatts (kW) across a span of three years (2012-2014), involving 370 individual clients in Portugal.
Preprocessing information:
- Grouped by hour (frequency: "1H").
- Applied Standardization as preprocessing technique ("Std").
Dataset information:
- Number of time series: 370
- Number of training samples: 26208
- Number of validation samples: 26256 (number_of_training_samples + 48)
- Number of testing samples: 26304 (number_of_validation_samples + 48)
Dataset format:
Dataset({
features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
num_rows: 370
})
Data format for a sample:
'start': datetime.datetime
'target': list of a time series data
'feat_static_cat': time series index
'feat_dynamic_real': None
'item_id': name of time series
Data example:
{'start': datetime.datetime(2012, 1, 1, 1, 0),
'target': [-0.19363673541224083, -0.08851588245610625, -0.19363673541224083, ... -0.5615597207587115,...],
'feat_static_cat': [0],
'feat_dynamic_real': None,
'item_id': 'MT_001'
}
Usage:
- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.
- Other algorithms can extract data directly by making use of 'target' feature.