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Datasets:
qingshufan
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GA-EVLRU

Tasks:
Time Series Forecasting
Languages:
English
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'Start'}) and 6 missing columns ({'month', 'second', 'minute', 'hour', 'year', 'day'}).

This happened while the csv dataset builder was generating data using

hf://datasets/qingshufan/GA-EVLRU/acn/demand.csv (at revision cf36c5f293c39d18670633930e64c90cf960fd92)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Start: string
              Demand: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 486
              to
              {'month': Value(dtype='int64', id=None), 'day': Value(dtype='int64', id=None), 'year': Value(dtype='int64', id=None), 'hour': Value(dtype='int64', id=None), 'minute': Value(dtype='int64', id=None), 'second': Value(dtype='int64', id=None), 'Demand': Value(dtype='int64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1438, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'Start'}) and 6 missing columns ({'month', 'second', 'minute', 'hour', 'year', 'day'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/qingshufan/GA-EVLRU/acn/demand.csv (at revision cf36c5f293c39d18670633930e64c90cf960fd92)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

month
int64
day
int64
year
int64
hour
int64
minute
int64
second
int64
Demand
int64
4
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End of preview.

Dataset Card for GA-EVLRU Battery Swapping Demand Datasets

Dataset Details

Dataset Description

This dataset consists of battery swapping demand datasets generated by the conversion of the ST-EVCDP, UrbanEV, and EV-Load-Open-Data. There are a total of nine scenario-specific datasets, each containing unstructured demand data (demand.csv) and data normalized to 1 hour (bss.csv). The dataset is used for the study of probability estimation and scheduling optimization for battery swap stations, where an innovative approach combining the Least Recently Used (LRU) strategy with genetic algorithms and a guided search mechanism is proposed to enhance global optimization capability.

Dataset Sources

Uses

Direct Use

The dataset can be directly used for research and development related to battery swapping demand prediction and optimization of battery swap stations. Specifically, it can be used to train and test the proposed probability estimation model and the GA-EVLRU algorithm for better understanding and predicting the battery swapping demand in different scenarios.

Out-of-Scope Use

The dataset is not intended for any applications unrelated to battery swapping demand prediction and optimization of battery swap stations. It should not be used for any commercial purposes that violate the GPL-3.0 license terms, such as selling the dataset without proper authorization. Also, it should not be used to generate any information that may cause harm or discrimination to individuals or groups.

Dataset Structure

Each of the nine datasets (acn, boulder_2021, dundee, palo_alto, paris, perth, sap, st-evcdp, urbanev) contains two main files: demand.csv which is the unstructured demand data, and bss.csv which is the data normalized to 1 hour. The source of the original data for these datasets is either from EV-Load-Open-Data, ST-EVCDP, or UrbanEV.

Dataset Creation

Curation Rationale

The motivation for creating this dataset is to provide a comprehensive set of data for studying the battery swapping demand in different scenarios, which can help in the development of more efficient battery swap station scheduling and optimization algorithms. By combining data from different sources and normalizing them, a more consistent and useful dataset for research is obtained.

Source Data

Data Collection and Processing

The data is collected from multiple open-source repositories: ST-EVCDP, UrbanEV, and EV-Load-Open-Data. The collected data is then processed to generate the battery swapping demand datasets. The processing includes data conversion to create the demand.csv and bss.csv files for each scenario. The specific processing steps are described in the associated research paper and the code available in the https://github.com/qingshufan/GA-EVLRU repository.

Who are the source data producers?

The source data producers for EV-Load-Open-Data are associated with the project by yvenn-amara. For ST-EVCDP, it is associated with the IntelligentSystemsLab. And for UrbanEV, it is also from the IntelligentSystemsLab.

Personal and Sensitive Information

There is no indication that the dataset contains personal or sensitive information such as addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.

Bias, Risks, and Limitations

The dataset may have limitations in representing all possible real-world scenarios for battery swapping demand. The data is collected from specific open-source repositories and may not cover all geographical locations, types of electric vehicles, or different charging patterns comprehensively. Also, the normalization process may introduce some biases in representing the actual demand in a completely accurate way.

Recommendations

Users should be aware that the dataset may not fully represent all real-world situations and should use it with caution when making decisions related to large-scale battery swap station deployments. When using the dataset for research, users should consider conducting sensitivity analyses to understand the impact of potential biases and limitations on their results.

Citation

BibTeX:

@inproceedings{li2025gaevlru,
      title={Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System}, 
      author={Anzhen Li and Shufan Qing and Xiaochang Li and Rui Mao and Mingchen Feng},
      journal={arXiv preprint arXiv:2504.07453},
      year={2025}
}

Dataset Card Contact

For any questions or further information about the dataset, you can contact the authors of the associated research paper or raise issues in the https://github.com/qingshufan/GA-EVLRU repository.

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