Datasets:

sicheng1806
/

new-energy-plant

Tasks:

Feature Extraction

Size:

100M<n<1B

License:

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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 12 new columns ({'power2', 'power11', 'power1', 'power3', 'power12', 'power5', 'power9', 'power8', 'power7', 'power10', 'power4', 'power6'}) and 11 missing columns ({'sensor_4', 'sensor_6', 'sensor_2', 'sensor_5', 'sensor_11', 'sensor_9', 'sensor_10', 'sensor_1', 'sensor_8', 'sensor_13', 'sensor_7'}).

This happened while the csv dataset builder was generating data using

hf://datasets/sicheng1806/new-energy-plant/W1_power.csv (at revision 742e3b86d65ba2222b6f75a0ba08edba6f54baac)

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 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, 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 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: double
              power1: double
              power2: double
              power3: double
              power4: double
              power5: double
              power6: double
              power7: double
              power8: double
              power9: double
              power10: double
              power11: double
              power12: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1746
              to
              {'Unnamed: 0': Value(dtype='float64', id=None), 'sensor_1': Value(dtype='float64', id=None), 'sensor_2': Value(dtype='float64', id=None), 'sensor_4': Value(dtype='float64', id=None), 'sensor_5': Value(dtype='float64', id=None), 'sensor_6': Value(dtype='float64', id=None), 'sensor_7': Value(dtype='float64', id=None), 'sensor_8': Value(dtype='float64', id=None), 'sensor_9': Value(dtype='float64', id=None), 'sensor_10': Value(dtype='float64', id=None), 'sensor_11': Value(dtype='float64', id=None), 'sensor_13': Value(dtype='float64', 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 1417, 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 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, 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 1741, 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 1872, 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 12 new columns ({'power2', 'power11', 'power1', 'power3', 'power12', 'power5', 'power9', 'power8', 'power7', 'power10', 'power4', 'power6'}) and 11 missing columns ({'sensor_4', 'sensor_6', 'sensor_2', 'sensor_5', 'sensor_11', 'sensor_9', 'sensor_10', 'sensor_1', 'sensor_8', 'sensor_13', 'sensor_7'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/sicheng1806/new-energy-plant/W1_power.csv (at revision 742e3b86d65ba2222b6f75a0ba08edba6f54baac)
              
              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.

Unnamed: 0 float64	sensor_1 float64	sensor_2 float64	sensor_4 float64	sensor_5 float64	sensor_6 float64	sensor_7 float64	sensor_8 float64	sensor_9 float64	sensor_10 float64	sensor_11 float64	sensor_13 float64
738,911,257	0.5666	0.5661	0.5766	0.5705	0.5634	0.5701	0.5682	0.5649	0.5702	0.5658	0.5768
738,911,258	0.5665	0.566	0.5765	0.5704	0.5633	0.57	0.5681	0.5648	0.572	0.5676	0.5748
738,911,259	0.5664	0.5658	0.5764	0.5703	0.5632	0.5699	0.5679	0.5646	0.5718	0.5675	0.5766
738,911,260	0.5663	0.5657	0.5762	0.5702	0.5631	0.5698	0.5678	0.5645	0.5717	0.5674	0.5746
738,911,261	0.5662	0.5656	0.5761	0.5701	0.5629	0.5697	0.5677	0.5644	0.5716	0.5673	0.5745
738,911,262	0.566	0.5655	0.576	0.57	0.5648	0.5695	0.5676	0.5643	0.5715	0.5691	0.5744
738,911,263	0.5659	0.5654	0.5778	0.5698	0.5627	0.5714	0.5675	0.5642	0.5714	0.569	0.5742
738,911,264	0.5658	0.5653	0.5758	0.5717	0.5645	0.5713	0.5674	0.5641	0.5712	0.5708	0.576
738,911,265	0.5657	0.5651	0.5756	0.5696	0.5644	0.5712	0.5672	0.5639	0.5711	0.5687	0.5759
738,911,266	0.5656	0.5669	0.5755	0.5695	0.5643	0.5691	0.5671	0.5638	0.571	0.5667	0.5758
738,911,267	0.5655	0.5649	0.5754	0.5713	0.5642	0.5709	0.5689	0.5637	0.5709	0.5666	0.5738
738,911,268	0.5653	0.5648	0.5753	0.5712	0.5621	0.5708	0.5669	0.5636	0.5708	0.5665	0.5736
738,911,269	0.5652	0.5665	0.5771	0.5711	0.564	0.5707	0.5668	0.5654	0.5707	0.5683	0.5735
738,911,270	0.5651	0.5646	0.5769	0.571	0.5638	0.5706	0.5667	0.5634	0.5705	0.5682	0.5753
738,911,271	0.5669	0.5645	0.5768	0.5709	0.5618	0.5705	0.5684	0.5633	0.5704	0.57	0.5752
738,911,272	0.5668	0.5643	0.5767	0.5707	0.5636	0.5703	0.5683	0.5631	0.5703	0.5699	0.5751
738,911,273	0.5667	0.5661	0.5766	0.5706	0.5635	0.5702	0.5682	0.563	0.5702	0.5697	0.5731
738,911,274	0.5665	0.5641	0.5765	0.5705	0.5634	0.5701	0.5681	0.5629	0.5719	0.5696	0.5748
738,911,275	0.5645	0.564	0.5763	0.5704	0.5633	0.57	0.5661	0.5628	0.57	0.5676	0.5747
738,911,276	0.5663	0.5657	0.5762	0.5722	0.5631	0.5699	0.5679	0.5646	0.5717	0.5675	0.5746
738,911,277	0.5662	0.5656	0.5761	0.5721	0.563	0.5698	0.5677	0.5645	0.5716	0.5654	0.5745
738,911,278	0.5661	0.5636	0.576	0.57	0.5648	0.5696	0.5676	0.5644	0.5715	0.5672	0.5744
738,911,279	0.5678	0.5654	0.5759	0.5719	0.5647	0.5715	0.5675	0.5623	0.5695	0.5671	0.5761
738,911,280	0.5658	0.5653	0.5758	0.5698	0.5627	0.5714	0.5674	0.5642	0.5712	0.5689	0.5741
738,911,281	0.5676	0.5652	0.5756	0.5697	0.5626	0.5713	0.5673	0.564	0.5711	0.5669	0.574
738,911,282	0.5675	0.5669	0.5755	0.5715	0.5644	0.5711	0.5672	0.5639	0.571	0.5668	0.5739
738,911,283	0.5655	0.5668	0.5773	0.5695	0.5623	0.5691	0.567	0.5638	0.5709	0.5667	0.5738
738,911,284	0.5673	0.5648	0.5772	0.5713	0.5622	0.5709	0.5669	0.5656	0.5708	0.5685	0.5755
738,911,285	0.5672	0.5647	0.577	0.5712	0.5621	0.5708	0.5668	0.5636	0.5706	0.5703	0.5754
738,911,286	0.567	0.5664	0.5769	0.5691	0.562	0.5687	0.5686	0.5635	0.5724	0.5682	0.5734
738,911,287	0.5669	0.5663	0.5749	0.5709	0.5619	0.5706	0.5666	0.5634	0.5704	0.57	0.5733
738,911,288	0.5668	0.5644	0.5767	0.5708	0.5637	0.5685	0.5665	0.5632	0.5703	0.568	0.5732
738,911,289	0.5667	0.5661	0.5747	0.5707	0.5636	0.5703	0.5664	0.5631	0.5702	0.5679	0.5731
738,911,290	0.5666	0.5641	0.5764	0.5706	0.5635	0.5682	0.5662	0.565	0.5701	0.5658	0.5748
738,911,291	0.5665	0.5659	0.5745	0.5685	0.5633	0.5681	0.5661	0.5629	0.5699	0.5657	0.5728
738,911,292	0.5663	0.5657	0.5762	0.5703	0.5632	0.57	0.5679	0.5628	0.5698	0.5656	0.5746
738,911,293	0.5662	0.5656	0.5742	0.5683	0.5631	0.5679	0.5678	0.5646	0.5715	0.5674	0.5763
738,911,294	0.5661	0.5655	0.576	0.5701	0.563	0.5697	0.5658	0.5626	0.5696	0.5673	0.5744
738,911,295	0.566	0.5654	0.5759	0.5681	0.5629	0.5696	0.5657	0.5624	0.5713	0.5672	0.5742
738,911,296	0.5659	0.5653	0.5757	0.5699	0.5628	0.5695	0.5674	0.5623	0.5694	0.5671	0.5741
738,911,297	0.5658	0.5652	0.5775	0.5698	0.5626	0.5694	0.5654	0.5642	0.5711	0.567	0.574
738,911,298	0.5657	0.5651	0.5755	0.5697	0.5625	0.5693	0.5653	0.5621	0.571	0.5668	0.5739
738,911,299	0.5655	0.5631	0.5754	0.5695	0.5624	0.5672	0.5671	0.5639	0.5709	0.5648	0.5738
738,911,300	0.5654	0.5648	0.5771	0.5675	0.5642	0.569	0.567	0.5619	0.5689	0.5647	0.5737
738,911,301	0.5653	0.5647	0.577	0.5674	0.5641	0.567	0.5669	0.5618	0.5688	0.5646	0.5735
738,911,302	0.5652	0.5646	0.575	0.5692	0.5621	0.5688	0.5649	0.5616	0.5705	0.5664	0.5734
738,911,303	0.567	0.5645	0.5768	0.5691	0.562	0.5667	0.5647	0.5635	0.5704	0.5663	0.5714
738,911,304	0.5668	0.5662	0.5767	0.569	0.5618	0.5666	0.5646	0.5614	0.5684	0.5662	0.5713
738,911,305	0.5667	0.5661	0.5747	0.5688	0.5617	0.5685	0.5664	0.5632	0.5702	0.566	0.5712
738,911,306	0.5666	0.566	0.5764	0.5668	0.5616	0.5684	0.5644	0.5612	0.5682	0.5659	0.573
738,911,307	0.5665	0.564	0.5745	0.5686	0.5615	0.5682	0.5643	0.5611	0.5681	0.562	0.5728
738,911,308	0.5664	0.5658	0.5743	0.5685	0.5614	0.5681	0.5661	0.561	0.568	0.5638	0.5727
738,911,309	0.5663	0.5638	0.5742	0.5684	0.5613	0.568	0.5641	0.5608	0.5697	0.5637	0.5726
738,911,310	0.5661	0.5637	0.5741	0.5663	0.5612	0.5679	0.5639	0.5607	0.5696	0.5674	0.5706
738,911,311	0.566	0.5636	0.574	0.5681	0.561	0.5678	0.5638	0.5606	0.5695	0.5653	0.5724
738,911,312	0.5659	0.5635	0.5739	0.568	0.5609	0.5677	0.5637	0.5605	0.5693	0.5671	0.5704
738,911,313	0.5658	0.5633	0.5756	0.5679	0.5627	0.5675	0.5636	0.5604	0.5692	0.5651	0.5703
738,911,314	0.5638	0.5632	0.5736	0.5659	0.5607	0.5674	0.5654	0.5603	0.5673	0.5631	0.572
738,911,315	0.5656	0.5631	0.5754	0.5696	0.5625	0.5673	0.5634	0.5602	0.5672	0.563	0.5719
738,911,316	0.5655	0.563	0.5734	0.5676	0.5605	0.5672	0.5651	0.56	0.5671	0.5629	0.5699
738,911,317	0.5635	0.5629	0.5733	0.5675	0.5604	0.5671	0.5631	0.5619	0.5669	0.5628	0.5698
738,911,318	0.5652	0.5628	0.575	0.5673	0.5622	0.567	0.563	0.5598	0.5668	0.5626	0.5697
738,911,319	0.5651	0.5627	0.5731	0.5672	0.5601	0.5669	0.5648	0.5597	0.5667	0.5644	0.5696
738,911,320	0.5631	0.5625	0.5748	0.5671	0.56	0.5667	0.5647	0.5596	0.5684	0.5643	0.5695
738,911,321	0.5649	0.5624	0.5747	0.567	0.5599	0.5666	0.5627	0.5614	0.5665	0.5642	0.5693
738,911,322	0.5648	0.5623	0.5746	0.5669	0.5598	0.5685	0.5645	0.5594	0.5664	0.5641	0.5692
738,911,323	0.5628	0.5622	0.5744	0.5668	0.5597	0.5664	0.5643	0.5593	0.5662	0.564	0.5691
738,911,324	0.5645	0.5639	0.5743	0.5686	0.5577	0.5663	0.5623	0.5591	0.5661	0.562	0.569
738,911,325	0.5644	0.562	0.5742	0.5665	0.5595	0.5681	0.5622	0.559	0.566	0.5618	0.5689
738,911,326	0.5643	0.5637	0.5741	0.5664	0.5574	0.566	0.564	0.5589	0.5659	0.5617	0.5688
738,911,327	0.5642	0.5636	0.5721	0.5663	0.5592	0.5659	0.5639	0.5588	0.5658	0.5616	0.5687
738,911,328	0.5641	0.5635	0.5739	0.5662	0.5572	0.5658	0.5638	0.5587	0.5675	0.5634	0.5685
738,911,329	0.5658	0.5634	0.5719	0.568	0.559	0.5657	0.5637	0.5586	0.5656	0.5652	0.5684
738,911,330	0.5639	0.5632	0.5718	0.566	0.557	0.5656	0.5617	0.5585	0.5654	0.5632	0.5683
738,911,331	0.5637	0.5631	0.5735	0.5659	0.5569	0.5655	0.5634	0.5583	0.5653	0.5631	0.5682
738,911,332	0.5655	0.563	0.5716	0.5657	0.5587	0.5654	0.5633	0.5582	0.5652	0.561	0.5681
738,911,333	0.5635	0.5629	0.5714	0.5656	0.5586	0.5652	0.5632	0.56	0.5651	0.5609	0.568
738,911,334	0.5634	0.5628	0.5732	0.5655	0.5584	0.5671	0.5612	0.5599	0.5668	0.5627	0.5679
738,911,335	0.5633	0.5627	0.5731	0.5654	0.5583	0.565	0.5648	0.5579	0.5649	0.5645	0.5677
738,911,336	0.5632	0.5626	0.5729	0.5653	0.5582	0.5649	0.5629	0.5578	0.5648	0.5644	0.5676
738,911,337	0.5631	0.5624	0.5728	0.5652	0.5581	0.5648	0.5627	0.5577	0.5646	0.5643	0.5675
738,911,338	0.5629	0.5623	0.5727	0.5631	0.558	0.5647	0.5626	0.5576	0.5645	0.5642	0.5674
738,911,339	0.561	0.5622	0.5726	0.563	0.5579	0.5646	0.5644	0.5574	0.5644	0.5622	0.5673
738,911,340	0.5609	0.5621	0.5725	0.5648	0.5578	0.5644	0.5624	0.5573	0.5643	0.562	0.5672
738,911,341	0.5607	0.562	0.5724	0.5647	0.5577	0.5643	0.5623	0.5591	0.566	0.56	0.5671
738,911,342	0.5606	0.5619	0.5722	0.5627	0.5575	0.5642	0.5622	0.559	0.5659	0.5618	0.5688
738,911,343	0.5587	0.5618	0.5721	0.5626	0.5574	0.5641	0.5621	0.5589	0.564	0.5617	0.5668
738,911,344	0.5604	0.5616	0.5702	0.5624	0.5573	0.564	0.5619	0.5588	0.5639	0.5616	0.5686
738,911,345	0.5584	0.5615	0.5701	0.5623	0.5572	0.5639	0.5618	0.5587	0.5656	0.5634	0.5684
738,911,346	0.5583	0.5614	0.5699	0.5622	0.559	0.5638	0.5617	0.5567	0.5654	0.5614	0.5683
738,911,347	0.5582	0.5613	0.5717	0.5621	0.557	0.5637	0.5616	0.5585	0.5653	0.5612	0.5682
738,911,348	0.5581	0.5612	0.5697	0.562	0.5588	0.5635	0.5615	0.5584	0.5652	0.5592	0.5681
738,911,349	0.558	0.5611	0.5696	0.5619	0.5568	0.5615	0.5595	0.5582	0.5651	0.5591	0.568
738,911,350	0.5579	0.561	0.5695	0.5618	0.5566	0.5614	0.5613	0.5581	0.565	0.5609	0.5679
738,911,351	0.5559	0.5609	0.5694	0.5597	0.5565	0.5613	0.5612	0.558	0.5649	0.5608	0.5678
738,911,352	0.5576	0.5607	0.5692	0.5596	0.5583	0.5612	0.561	0.556	0.5648	0.5607	0.5676
738,911,353	0.5575	0.5606	0.5691	0.5595	0.5582	0.561	0.5591	0.5559	0.5646	0.5625	0.5675
738,911,354	0.5574	0.5605	0.569	0.5594	0.5562	0.5609	0.5589	0.5558	0.5645	0.5605	0.5674
738,911,355	0.5573	0.5604	0.5671	0.5593	0.558	0.5608	0.5607	0.5576	0.5644	0.5603	0.5673
738,911,356	0.5553	0.5603	0.5688	0.5592	0.5579	0.5626	0.5587	0.5575	0.5643	0.5564	0.5672

End of preview.

New Energy Plant

Dataset from problem D of ACMCM 2024.

Wind farm dataset W1
- Data: wind velocity and power output
- Number of turbines: 12
- Rated power: 2.x MW
- Size of the wind farm: 4x4 km^2
- Length of data: one month (31. July 2009 - 29. Aug. 2009)
- Sample frequency: 1 Hz
This data set was kindly provided by wpd windmanager GmbH, Bremen, Germany.
Solar data set S1
- Data: solar irradiance
- Number of sensors: 11
- Length of data: One month (01. June 1993 – 30. June 1993)
- Sample frequency: 1 Hz
This data set was recorded on a platform on the roof of the university of Oldenburg, Germany.

Downloads last month: 33