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---
license: cc-by-4.0
---
# Timeseries Data Processing
This repository contains a script for loading and processing time series data using the `datasets` library and converting it to a pandas DataFrame for further analysis.
## Dataset
The dataset used contains time series data with the following features:
- **`id`**: Identifier for the dataset, formatted as `Country_Number of Household` (e.g., `GE_1` for Germany, household 1).
- **`datetime`**: Timestamp indicating the date and time of the observation.
- **`target`**: Energy consumption measured in kilowatt-hours (kWh).
- **`category`**: The resolution of the time series (e.g., 15 minutes, 30 minutes, 60 minutes).
## Data Sources
The research uses raw data from the following open-source databases:
- **Netherlands Smart Meter Data**: [Liander Open Data](https://www.liander.nl/partners/datadiensten/open-data/data)
- **UK Smart Meter Data**: [London Datastore](https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households)
- **Germany Smart Meter Data**: [Open Power System Data](https://data.open-power-system-data.org/household_data/2020-04-15)
- **Australian Smarter Data**:[Smart-Grid Smart-City Customer Trial Data](https://data.gov.au/dataset/ds-dga-4e21dea3-9b87-4610-94c7-15a8a77907ef/details)
## Requirements
- Python 3.6+
- `datasets` library
- `pandas` library
You can install the required libraries using pip:
```sh
python -m pip install "dask[complete]" # Install everything
```
## Usage
The following example demonstrates how to load the dataset and convert it to a pandas DataFrame.
```python
import dask.dataframe as dd
# read parquet file
df = dd.read_parquet("hf://datasets/Weijie1996/load_timeseries/30m_resolution_ge/ge_30m.parquet")
# change to pandas dataframe
df = df.compute()
```
## Output
``` data
id datetime target category
0 NL_1 2013-01-01 00:00:00 0.117475 60m
1 NL_1 2013-01-01 01:00:00 0.104347 60m
2 NL_1 2013-01-01 02:00:00 0.103173 60m
3 NL_1 2013-01-01 03:00:00 0.101686 60m
4 NL_1 2013-01-01 04:00:00 0.099632 60m
```
## Related Work
This dataset has been utilized in the following research studies:
1. **Comparative Assessment of Generative Models for Transformer- and Consumer-Level Load Profiles Generation**
- GitHub Repository: [Generative Models for Customer Profile Generation](https://github.com/xiaweijie1996/Generative-Models-for-Customer-Profile-Generation)
2. **A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction**
- GitHub Repository: [Full Convolutional Profile Flow](https://github.com/xiaweijie1996/Full-Convolutional-Profile-Flow?tab=readme-ov-file)
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