SPP Hourly Actual Load

Actual hourly electricity demand in SPP, one row per hour and balancing area: event_time, baa ('SPP' for the RTO East footprint, 'SWPW' for RTO West from 2026-04), system_mw and source.

Load data SPP market

Query with the agent

Native record

balancing area x operating hour

hourly publication cadence

Evidence

Observed market data

Published observations from the named source

Time basis

Interval-start records

UTC · event_time

Overview

140k rows, 2011-01 to now. Two feeds build it and `source` says which: 'zonal_hourly' rows (2011 to 2016, plus fallback hours later) are the sum of 17 load-zone columns (okge_mw, wfec_mw, wr_mw, waue_mw, les_mw, ede_mw, seci_mw, kacy_mw, mps_mw, csws_mw, sprm_mw, indn_mw, sps_mw, oppd_mw, kcpl_mw, nppd_mw, grda_mw); 'system_5m' rows (2017 on) average SPP's complete five-minute actuals and carry no zones.

Query contract

One row per (event_time, baa) is already the chosen observation: a complete five-minute hour wins over the zonal feed. Filter baa first.

Analysis rules

Select baa='SPP' for the East footprint, which is the whole RTO before 2026-04. Add 'SWPW' only for a question about the combined RTO after the western expansion, and say so.
Zone columns are NULL in system_5m rows. NULL is a feed without zones, not zero demand; a zone question can be answered only from zonal_hourly rows.

Related analysis

forecast error for a delivered hour · join on event_time and baa against mid_term rows from one issue vintage

Interpretation

The table changes shape at 2017-01, where the zonal feed gives way to the five-minute system feed. A long trend across that seam compares two measurements; say so.
Hourly values are interval averages, not instantaneous readings.

Access

Query from your own tools

Use the guarded HTTP endpoint directly, or connect an MCP client and load the dataset contract with describe_data before calling query_data.

SPP MCP API reference

HTTP endpoint

POST /api/v1/datasets/spp_load/query

Need the full history of this dataset in Snowflake, Databricks or your own storage? Data services delivers it on a schedule.

Schema21

Column

Type

event_timeDateTime64(6, 'UTC')

baaLowCardinality(String)

system_mwNullable(Float64)

sourceLowCardinality(String)

okge_mwNullable(Float64)

wfec_mwNullable(Float64)

wr_mwNullable(Float64)

waue_mwNullable(Float64)

les_mwNullable(Float64)

ede_mwNullable(Float64)

seci_mwNullable(Float64)

kacy_mwNullable(Float64)

mps_mwNullable(Float64)

csws_mwNullable(Float64)

sprm_mwNullable(Float64)

indn_mwNullable(Float64)

sps_mwNullable(Float64)

oppd_mwNullable(Float64)

kcpl_mwNullable(Float64)

nppd_mwNullable(Float64)

grda_mwNullable(Float64)

Sample data

event_time	baa	system_mw	source	okge_mw	wfec_mw	wr_mw	waue_mw	les_mw	ede_mw	seci_mw	kacy_mw	mps_mw	csws_mw	sprm_mw	indn_mw	sps_mw	oppd_mw	kcpl_mw	nppd_mw	grda_mw
2026-10-17 03:00:00.000000	SWPW	2759.9166666666665	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-17 03:00:00.000000	SPP	29873.583333333332	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-17 02:00:00.000000	SWPW	2800.8333333333335	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-17 02:00:00.000000	SPP	30624.666666666668	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-17 01:00:00.000000	SPP	31247.583333333332	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-17 01:00:00.000000	SWPW	2838.8333333333335	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-17 00:00:00.000000	SWPW	2802.4166666666665	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-17 00:00:00.000000	SPP	31577.083333333332	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 23:00:00.000000	SPP	31293.75	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 23:00:00.000000	SWPW	2697.4166666666665	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 22:00:00.000000	SPP	31281.166666666668	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 22:00:00.000000	SWPW	2597.5	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 21:00:00.000000	SWPW	2532.9166666666665	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 21:00:00.000000	SPP	31283.5	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 20:00:00.000000	SPP	31397.25	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 20:00:00.000000	SWPW	2524.4166666666665	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 19:00:00.000000	SPP	31613.75	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 19:00:00.000000	SWPW	2523.6666666666665	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 18:00:00.000000	SPP	31824.25	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 18:00:00.000000	SWPW	2538.0833333333335	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 17:00:00.000000	SWPW	2580.5	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 17:00:00.000000	SPP	31924.166666666668	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 16:00:00.000000	SPP	32005.75	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 16:00:00.000000	SWPW	2645.75	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅
2026-10-16 15:00:00.000000	SPP	31866.083333333332	system_5m	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅	∅

Coverage

No time-indexed data — coverage isn’t applicable to this dataset.

Related load data

Related load data
Dataset	Description
Actual System LoadSPP · 5-minute	SPP's realized system-wide demand in MW at five-minute resolution, the actual-load counterpart to the short-term and mid-term load forecasts. Note that the feed republishes the same event_time on successive polls with a fresh ingested_at, so a read should collapse repeats on ingested_at before use.
Mid-Term Load Forecast (MTLF)SPP · Hourly, out ~7 days	SPP's mid-term load forecast in MW, running roughly seven days ahead, carried alongside the realized actual for the same interval so forecast error can be computed within the table. Bitemporal: each row records the delivery interval and the vintage the forecast was published at, retaining every successive forecast for a given hour.
Short-Term Load Forecast (STLF)SPP · 5-minute, frequent vintages	SPP's short-term load forecast in MW. Bitemporal: each row carries both the delivery interval (event_time) and the vintage the forecast was issued at (publish_time), so successive forecasts for the same interval are retained rather than overwritten. That makes the table usable for forecast-error and forecast-convergence analysis, not only for the latest view.
SPP Load ForecastSPP · reissued per vintage; two products at different resolutions	SPP's demand forecast in long form: issued_at (when SPP published it), event_time (the hour or interval being forecast), horizon, baa (SPP East or SWPW West), and forecast_mw. History reaches 2017, with named East/West footprints available around the 2026 expansion; older blank BAA rows have unknown footprint. Targets run out to seven days ahead. The horizon column separates SPP's two published products: 'short_term' (STLF, five-minute targets) and 'mid_term' (MTLF, hourly targets out seven days). They are different forecasts at different resolutions and must never be mixed in one series. Issue time and target time are separate columns, so this table supports both 'what does SPP expect' and 'what did SPP expect N hours out'.