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ERCOT Mid-Term Load Forecast (MTLF)

ERCOT's mid-term load forecast (MTLF) for the Texas grid, giving the expected electricity demand for each of the next seven days at hourly resolution.

ercot.load_forecastLoad dataERCOT market
One row per
weather zone x target hour x forecast vintage x model
Hourly (7-day forecast) cadence
Evidence
Forecast data
Forward-looking values with an explicit issue time
Time basis
Interval-end records
UTC · event_time

Overview

The forecast is broken out by ERCOT's eight weather zones plus a system-wide total, and a new vintage is issued every hour, so you can also see how the outlook for a given day shifts as it approaches. Operators use this to plan generation and reserves, while traders and researchers use it to anticipate price and tightness conditions and to study how forecasts evolve and where they miss. The official source is ERCOT report NP3-565-CD, Seven-Day Load Forecast by Weather Zone.

Query contract
For each target hour, select the row flagged in use from the latest source_time vintage that was actually available by the analysis cutoff. For forecast accuracy, source_time must be no later than event_time; keep both columns in the result so issue time and target time remain distinct.
Analysis rules
Do not sum forecast vintages.
Interpretation
system_total_mw is a forecast of gross system electricity demand, not net load and not the generation required after renewable output. Actual-minus-forecast is a gross-demand forecast error; a wind or solar generation shortfall does not by itself cause that error.Never evaluate a historical forecast with a source_time after its event_time. That is a hindsight vintage, not a forecast that operators could have used.

Access

Plans
See available download and API access options. View pricing
MCP
Connect an MCP client and load the contract with describe_data before calling query_data.ERCOT MCPAPI reference
HTTP endpoint
POST /api/v1/datasets/ercot_load_forecast/query
Full history
Delivered to Snowflake, Databricks or your own storage on a schedule by Data services.
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Schema

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Sample data

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Coverage

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