Load Data Examples#

Walkthrough on how to query Load data from PJM using gridstatus#

import gridstatus
iso = gridstatus.PJM()

Get Load Data#

Get 5 Minute frequency load data for PJM and the individual zones within PJM. The column named “Load” is for entire PJM RTO and is followed by columns for each zone.

df = iso.get_load(date="today")
df
Time Load AE AEP APS ATSI BC COMED DAYTON DEOK ... PE PEP PJM MID ATLANTIC REGION PJM RTO PJM SOUTHERN REGION PJM WESTERN REGION PL PN PS RECO
0 2023-02-19 00:04:52.240000-05:00 85820.695312 1081.911010 14136.366210 5669.723632 6847.440917 3385.183837 9609.372070 1687.599365 2614.332519 ... 3963.656494 3163.468261 28487.888671 85820.695312 13781.175781 41903.289062 4600.699218 1749.422851 4306.609863 132.458816
1 2023-02-19 00:09:55.170000-05:00 85672.929687 1082.672851 14194.285156 5668.672851 6817.770507 3363.967773 9441.746093 1686.015991 2657.982177 ... 3946.796142 3149.341552 28392.376953 85672.929687 13805.767578 41820.437500 4553.702636 1763.478271 4285.686523 131.868545
2 2023-02-19 00:14:58.240000-05:00 85787.500000 1076.975708 14345.359375 5693.860839 6943.626953 3349.864013 9447.319335 1679.879150 2617.458740 ... 3942.522216 3160.395751 28288.671875 85787.500000 13785.943359 42033.507812 4577.664550 1757.319335 4263.410644 131.263671
3 2023-02-19 00:19:55.170000-05:00 85386.046875 1070.951538 14228.362304 5640.020996 6833.658203 3354.219482 9390.935546 1687.029785 2620.173583 ... 3942.753173 3153.634765 28261.566406 85386.046875 13750.986328 41727.281250 4552.393554 1725.664184 4263.164062 131.169281
4 2023-02-19 00:24:56.230000-05:00 85363.523437 1072.070678 14457.824218 5623.041015 6882.307617 3338.153076 9372.326171 1674.317993 2618.351806 ... 3916.544677 3123.846679 28138.189453 85363.523437 13736.481445 41985.210937 4542.468261 1729.742675 4235.181152 129.811752
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
206 2023-02-19 17:14:56.370000-05:00 80829.437500 1128.501953 12913.198242 5090.649414 6754.774902 3163.908691 8772.684570 1630.271118 2507.304199 ... 4108.909667 2897.237060 28304.431640 80829.437500 12192.932617 39000.917968 4620.941894 1827.680786 4413.996582 141.634582
207 2023-02-19 17:19:56.350000-05:00 81491.000000 1130.316040 13027.240234 5152.086914 6811.769042 3188.087402 8866.217773 1633.950317 2508.971191 ... 4133.876464 2905.732666 28419.800781 81491.000000 12313.500976 39397.351562 4649.315917 1824.025634 4447.368164 142.517562
208 2023-02-19 17:24:56.360000-05:00 81557.664062 1143.872070 13049.479492 5154.691406 6767.947753 3200.821044 8875.156250 1654.186035 2520.479736 ... 4158.643554 2919.845703 28599.712890 81557.664062 12184.108398 39407.933593 4650.166992 1835.580566 4489.407226 143.232330
209 2023-02-19 17:29:58.370000-05:00 81946.515625 1150.359619 12946.251953 5197.521484 6941.558105 3231.901367 8930.020507 1653.093750 2559.698242 ... 4162.666015 2949.316162 28697.187500 81946.515625 12246.151367 39623.742187 4699.521972 1827.781982 4486.046875 144.616775
210 2023-02-19 17:34:58.390000-05:00 82293.460937 1155.380981 13096.729492 5209.138671 6853.371582 3241.266601 8914.225585 1634.077026 2548.561767 ... 4184.200195 2947.689208 28849.355468 82293.460937 12406.890625 39614.972656 4684.597656 1813.053100 4530.397460 146.449813

211 rows × 26 columns

fig = gridstatus.viz.load_over_time(df, iso="PJM")
fig.show("svg", width=1200, height=600)
../../_images/d653bfe2b324d9f17ca3e89cb1518bdd93aa8ff8b57041c550d028d3f1739362.svg

Query for Date Range#

df = iso.get_load(start="Feb 1, 2023", end="Feb 20, 2023")
df
100%|██████████| 2/2 [00:02<00:00,  2.85s/it]
Time Load AE AEP APS ATSI BC COMED DAYTON DEOK ... PE PEP PJM MID ATLANTIC REGION PJM RTO PJM SOUTHERN REGION PJM WESTERN REGION PL PN PS RECO
0 2023-02-01 00:04:55.470000-05:00 94584.773437 1075.860839 15904.731445 6323.931152 8323.074218 3419.240234 12101.963867 2251.489257 3111.912353 ... 4139.106445 3237.361816 29806.378906 94584.773437 13124.350585 49499.585937 5031.371582 2070.169433 4478.117187 142.042251
1 2023-02-01 00:09:56.690000-05:00 94476.171875 1071.402343 15994.041015 6358.305175 8337.112304 3412.146484 12082.160156 2234.879394 3103.661132 ... 4125.386718 3218.280029 29684.619140 94476.171875 13049.120117 49586.527343 5017.970214 2076.222900 4470.617675 141.239334
2 2023-02-01 00:14:56.690000-05:00 94384.820312 1064.398681 16107.166015 6280.489257 8120.046875 3400.794921 12124.920898 2230.133056 3093.563964 ... 4065.927001 3219.549316 29578.138671 94384.820312 13197.892578 49467.988281 5005.752441 2079.036132 4475.640136 140.498703
3 2023-02-01 00:19:55.500000-05:00 94051.085937 1061.407226 16091.453125 6203.060058 8096.914550 3385.431396 12024.380859 2233.240722 3102.755615 ... 4121.025390 3195.672607 29515.658203 94051.085937 13170.411132 49245.992187 4994.878417 2022.717529 4442.506347 139.327087
4 2023-02-01 00:24:56.670000-05:00 93737.414062 1058.097656 15843.791992 6268.982910 8063.426269 3360.377685 12087.929687 2225.082519 3123.419189 ... 4100.353515 3179.604003 29481.009765 93737.414062 13096.277343 49113.949218 4983.544433 2070.152343 4443.084960 139.987518
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
5381 2023-02-19 17:14:56.370000-05:00 80829.437500 1128.501953 12913.198242 5090.649414 6754.774902 3163.908691 8772.684570 1630.271118 2507.304199 ... 4108.909667 2897.237060 28304.431640 80829.437500 12192.932617 39000.917968 4620.941894 1827.680786 4413.996582 141.634582
5382 2023-02-19 17:19:56.350000-05:00 81491.000000 1130.316040 13027.240234 5152.086914 6811.769042 3188.087402 8866.217773 1633.950317 2508.971191 ... 4133.876464 2905.732666 28419.800781 81491.000000 12313.500976 39397.351562 4649.315917 1824.025634 4447.368164 142.517562
5383 2023-02-19 17:24:56.360000-05:00 81557.664062 1143.872070 13049.479492 5154.691406 6767.947753 3200.821044 8875.156250 1654.186035 2520.479736 ... 4158.643554 2919.845703 28599.712890 81557.664062 12184.108398 39407.933593 4650.166992 1835.580566 4489.407226 143.232330
5384 2023-02-19 17:29:58.370000-05:00 81946.515625 1150.359619 12946.251953 5197.521484 6941.558105 3231.901367 8930.020507 1653.093750 2559.698242 ... 4162.666015 2949.316162 28697.187500 81946.515625 12246.151367 39623.742187 4699.521972 1827.781982 4486.046875 144.616775
5385 2023-02-19 17:34:58.390000-05:00 82293.460937 1155.380981 13096.729492 5209.138671 6853.371582 3241.266601 8914.225585 1634.077026 2548.561767 ... 4184.200195 2947.689208 28849.355468 82293.460937 12406.890625 39614.972656 4684.597656 1813.053100 4530.397460 146.449813

5386 rows × 26 columns

fig = gridstatus.viz.load_over_time(df, iso="PJM")
fig.show("svg", width=1200, height=600)
../../_images/cb3c9612519cc626e0ded9a0e2a900ac18683d288c2eef27a68f2c9dabd355da.svg