README.md

---
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**:
```python
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**:
```python
  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**:
```python
{'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.