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

Train

@kaggle.trnderenergikraft_grid_loss_time_series_dataset.train

3.07 MB
17520 rows
39 columns

unnamed_0 Unnamed: 0	demand Demand	grid1_load Grid1-load	grid1_loss Grid1-loss	grid1_temp Grid1-temp	grid2_load Grid2-load	grid2_loss Grid2-loss	grid2_1_temp Grid2–1-temp	grid2_2_temp Grid2–2-temp	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
Fri Dec 01 2017 00:00:00 GMT+0000 (Coordinated Universal Time)	351.2253662774	449.532	18.766	267.85	193.3822	5.586	262.274810791016	262.578399658203	265.232360839844	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	1
Fri Dec 01 2017 01:00:00 GMT+0000 (Coordinated Universal Time)	344.4204580372	439.036	18.02	267.55	189.39140000000003	5.464	262.461517333984	262.839324951172	265.247192382813	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	0.9659258262890684	0.2588190451025207
Fri Dec 01 2017 02:00:00 GMT+0000 (Coordinated Universal Time)	342.4132136714	442.237	18.246	267.25	170.54269999999997	4.922	262.358520507813	262.623046875	265.378570556641	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	0.8660254037844387	0.4999999999999999
Fri Dec 01 2017 03:00:00 GMT+0000 (Coordinated Universal Time)	345.4270710594	447.836	18.645	266.95	187.454	5.406	262.498352050781	262.581237792969	265.624847412109	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	0.7071067811865476	0.7071067811865475
Fri Dec 01 2017 04:00:00 GMT+0000 (Coordinated Universal Time)	357.5612573713	463.995	19.823	267.05	189.6965	5.473	262.377746582031	262.526062011719	265.755218505859	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	0.5000000000000001	0.8660254037844386
Fri Dec 01 2017 05:00:00 GMT+0000 (Coordinated Universal Time)	393.1512836735	514.748	23.797	267.15	199.70879999999997	5.785	261.80810546875	262.581420898438	266.405303955078	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	0.2588190451025207	0.9659258262890684
Fri Dec 01 2017 06:00:00 GMT+0000 (Coordinated Universal Time)	445.3529003866	576.118	29.153	267.25	212.01699999999997	6.19	260.785278320313	263.041656494141	266.063079833984	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	6.123233995736766e-17	1
Fri Dec 01 2017 07:00:00 GMT+0000 (Coordinated Universal Time)	464.8048818072	593.987	30.826	267.35	218.1405	6.401	260.625701904297	263.413482666016	267.065612792969	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	-0.2588190451025206	0.9659258262890684
Fri Dec 01 2017 08:00:00 GMT+0000 (Coordinated Universal Time)	465.8348277152	591.086	30.551	267.45	218.4997	6.413	260.9248046875	263.839721679688	267.047332763672	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	-0.4999999999999998	0.8660254037844387
Fri Dec 01 2017 09:00:00 GMT+0000 (Coordinated Universal Time)	465.8714191208	588.167	30.275	267.55	216.4281	6.341	260.08251953125	264.411651611328	267.459930419922	-1.8369701987210294e-16	-1	0.8660254037844384	-0.5000000000000004	0.8229838658936564	-0.5680647467311559	-0.9009688679024191	-0.433883739117558	-0.7071067811865475	0.7071067811865476


CREATE TABLE train (
  "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
);