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