Grid Loss Prediction Dataset

A time series dataset for predicting loss in three electrical grids in Norway

@kaggle.trnderenergikraft_grid_loss_time_series_dataset

Table List

3 tables(4.98 MB)

Test

Test Backfilled Missing Data

Train

Test Backfilled Missing Data

@kaggle.trnderenergikraft_grid_loss_time_series_dataset.test_backfilled_missing_data

977.24 KB
4369 rows
39 columns

unnamed_0 Unnamed: 0	demand Demand	grid1_load Grid1-load	grid1_loss Grid1-loss	grid1_loss_prophet_daily Grid1-loss-prophet-daily	grid1_loss_prophet_pred Grid1-loss-prophet-pred	grid1_loss_prophet_trend Grid1-loss-prophet-trend	grid1_loss_prophet_weekly Grid1-loss-prophet-weekly	grid1_loss_prophet_yearly Grid1-loss-prophet-yearly	grid1_temp Grid1-temp	grid2_load Grid2-load	grid2_loss Grid2-loss	grid2_loss_prophet_daily Grid2-loss-prophet-daily	grid2_loss_prophet_pred Grid2-loss-prophet-pred	grid2_loss_prophet_trend Grid2-loss-prophet-trend	grid2_loss_prophet_weekly Grid2-loss-prophet-weekly	grid2_loss_prophet_yearly Grid2-loss-prophet-yearly	grid2_1_temp Grid2–1-temp	grid2_2_temp Grid2–2-temp	grid3_load Grid3-load	grid3_loss Grid3-loss	grid3_loss_prophet_daily Grid3-loss-prophet-daily	grid3_loss_prophet_pred Grid3-loss-prophet-pred	grid3_loss_prophet_trend Grid3-loss-prophet-trend	grid3_loss_prophet_weekly Grid3-loss-prophet-weekly	grid3_loss_prophet_yearly Grid3-loss-prophet-yearly	grid3_temp Grid3-temp	season_x Season X	season_y Season Y	month_x Month X	month_y Month Y	week_x Week X	week_y Week Y	weekday_x Weekday X	weekday_y Weekday Y	hour_x Hour X	hour_y Hour Y
Sun Dec 01 2019 00:00:00 GMT+0000 (Coordinated Universal Time)	314.3962358406	407.676	21.5521	-3.93876	25.6415	23.9576	-2.50138	8.12396	272.85	137.25	15.6814	-0.760133	15.3324	24.4363	-0.640087	-7.70368	267.34967	271.9261	13.2914	2.25285	-0.0289238	1.97538	1.63163	0.00850268	0.364172	272.9683	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	1
Sun Dec 01 2019 01:00:00 GMT+0000 (Coordinated Universal Time)	305.6917392306	397.966	20.7119	-4.41943	25.192	23.9586	-2.48713	8.13994	273.05	137.687	15.6578	-0.858023	15.2357	24.4408	-0.642784	-7.70428	267.62598	271.78638	14.784	2.14484	-0.034544	1.96918	1.63178	0.00785817	0.364089	272.9277	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	0.9659258262890684	0.2588190451025207
Sun Dec 01 2019 02:00:00 GMT+0000 (Coordinated Universal Time)	300.4315347665	392.904	20.2734	-4.49229	25.1594	23.9595	-2.46375	8.15594	273.15	137.365	15.5844	-0.861561	15.2358	24.4453	-0.643067	-7.70486	266.46542	271.02573	13.8604	2.0458	-0.0369842	1.96612	1.63193	0.0071725	0.364007	273.12994	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	0.8660254037844387	0.4999999999999999
Sun Dec 01 2019 03:00:00 GMT+0000 (Coordinated Universal Time)	297.0648071672	392.726	20.2537	-3.91784	25.7832	23.9604	-2.43132	8.17194	273.25	137.616	15.5618	-0.716442	15.387	24.4498	-0.640899	-7.70542	265.3127	270.74948	13.6468	2.02524	-0.0319102	1.97054	1.63207	0.00645058	0.363926	273.51044	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	0.7071067811865476	0.7071067811865475
Sun Dec 01 2019 04:00:00 GMT+0000 (Coordinated Universal Time)	288.4636654639	394.478	20.4055	-2.60199	27.1573	23.9614	-2.38999	8.18795	273.35	137.43	15.4893	-0.413884	15.6982	24.4543	-0.636258	-7.70596	265.47272	271.29572	13.361	1.90034	-0.0175498	1.98422	1.63222	0.00569765	0.363846	273.78616	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	0.5000000000000001	0.8660254037844386
Sun Dec 01 2019 05:00:00 GMT+0000 (Coordinated Universal Time)	299.4410925248	402.027	21.0555	-0.794922	29.0314	23.9623	-2.33996	8.20397	273.45	137.455	15.6575	-0.0269303	16.0962	24.4588	-0.629138	-7.70648	265.3952	272.35736	13.673	2.08089	0.00238198	2.00344	1.63237	0.00491927	0.363766	273.59244	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	0.2588190451025207	0.9659258262890684
Sun Dec 01 2019 06:00:00 GMT+0000 (Coordinated Universal Time)	328.1615387639	416.15	22.309	0.953783	30.8555	23.9632	-2.2815	8.22	273.45	137.423	15.7985	0.315531	16.4522	24.4632	-0.619549	-7.70699	266.8636	272.44553	14.3719	1.88934	0.0200494	2.02038	1.63252	0.00412125	0.363688	273.05902	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	6.123233995736766e-17	1
Sun Dec 01 2019 07:00:00 GMT+0000 (Coordinated Universal Time)	350.5611777071	442	24.7461	2.09721	32.0825	23.9642	-2.21495	8.23604	273.15	138.316	16.192	0.504638	16.6574	24.4677	-0.607518	-7.70748	267.88293	272.87405	16.0906	1.86285	0.0284114	2.028	1.63266	0.0033096	0.36361	273.25122	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	-0.2588190451025206	0.9659258262890684
Sun Dec 01 2019 08:00:00 GMT+0000 (Coordinated Universal Time)	367.2935113395	451.307	25.6571	2.42683	32.5033	23.9651	-2.14066	8.25209	272.85	151.107	16.56	0.517928	16.6891	24.4722	-0.593089	-7.70795	268.3263	273.3174	15.0319	2.06019	0.0257375	2.02457	1.63281	0.00249046	0.363534	272.9602	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	-0.4999999999999998	0.8660254037844387
Sun Dec 01 2019 09:00:00 GMT+0000 (Coordinated Universal Time)	376.9657496135	463.188	26.8228	2.17234	32.3474	23.966	-2.05908	8.26814	272.55	153.798	16.8755	0.422627	16.6146	24.4767	-0.576321	-7.7084	268.30804	272.91733	16.0136	2.1533	0.0161248	2.01421	1.63296	0.00167007	0.363458	272.7486	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	0.6234898018587334	-0.7818314824680299	-0.7071067811865475	0.7071067811865476


CREATE TABLE test_backfilled_missing_data (
  "unnamed_0" TIMESTAMP,
  "demand" DOUBLE,
  "grid1_load" DOUBLE,
  "grid1_loss" DOUBLE,
  "grid1_loss_prophet_daily" DOUBLE,
  "grid1_loss_prophet_pred" DOUBLE,
  "grid1_loss_prophet_trend" DOUBLE,
  "grid1_loss_prophet_weekly" DOUBLE,
  "grid1_loss_prophet_yearly" DOUBLE,
  "grid1_temp" DOUBLE,
  "grid2_load" DOUBLE,
  "grid2_loss" DOUBLE,
  "grid2_loss_prophet_daily" DOUBLE,
  "grid2_loss_prophet_pred" DOUBLE,
  "grid2_loss_prophet_trend" DOUBLE,
  "grid2_loss_prophet_weekly" DOUBLE,
  "grid2_loss_prophet_yearly" DOUBLE,
  "grid2_1_temp" DOUBLE,
  "grid2_2_temp" DOUBLE,
  "grid3_load" DOUBLE,
  "grid3_loss" DOUBLE,
  "grid3_loss_prophet_daily" DOUBLE,
  "grid3_loss_prophet_pred" DOUBLE,
  "grid3_loss_prophet_trend" DOUBLE,
  "grid3_loss_prophet_weekly" DOUBLE,
  "grid3_loss_prophet_yearly" DOUBLE,
  "grid3_temp" DOUBLE,
  "season_x" DOUBLE,
  "season_y" DOUBLE,
  "month_x" DOUBLE,
  "month_y" DOUBLE,
  "week_x" DOUBLE,
  "week_y" DOUBLE,
  "weekday_x" DOUBLE,
  "weekday_y" DOUBLE,
  "holiday" BIGINT,
  "hour_x" DOUBLE,
  "hour_y" DOUBLE,
  "has_incorrect_data" BOOLEAN
);