October in ERCOT brought a mix of seasonal challenges—unusually warm temperatures early in the month, a sharp cooldown later, and notable variability in renewable generation. These conditions tested the accuracy of short-term and day-ahead forecasts, which are essential for power market participants and traders managing risk and market operations. In this review, we highlight how Enverus consistently delivered superior performance compared to ERCOT load, wind and solar forecast, providing actionable insights for power trading strategies.
Load Forecast
This last October in Texas brought very unusual hot temperatures during the first 11 days. In Houston, for example, the average high was above 90°F, which is well above normal. These hot temperatures caused the total demand across Texas to exceed 71,000 MW during most days.
Around mid-October, the weather pattern began to shift but it was not until the end of the month that temperatures dropped significantly. The most notable change happened between Oct. 28 and Oct. 29. On Oct. 28, Houston high temperatures were still above 88°F, with lows near 70°F. Over the next three days, high temperatures dropped to around 70°F and lows to about 50°F. This sudden change in the temperatures caused ERCOT peak demand to fall from 62,720 MW on Oct. 28 to below 52,000 MW the following day. Our day-ahead load forecast captured this sudden demand drop with exceptional accuracy, achieving a MAPE of 1.35% on Oct. 29, compared to the ISO forecast’s 5.76%. This strong performance continued through the end of the month, with our forecast maintaining a MAPE of 2.33%, again outperforming ISO’s 3.06%.
Wind Forecast
Wind generation remained highly volatile in Texas across the whole month of October. On Oct. 27 wind generation dropped to values below 2,600 for a few hours but the next day increased above 22,000 MW for extended periods. Then, on Oct. 29, wind generation started a decreasing trend, falling below 10,000 MW by the end of the day. And, on Oct. 30 it stayed under 4,000 MW for several hours. Such fluctuations are notoriously difficult to predict. However, our day-ahead wind forecast tracked these changes far better than ISO’s.
| DATE | MAE ENVERUS DAY AHEAD | MAE ISO DAY AHEAD | CAP_MAPE ENVERUS DAY AHEAD* | CAP_MAPE ISO DAY AHEAD* |
| Oct. 27 | 1760.83 MW | 2138.85 MW | 6.23% | 7.57% |
| Oct. 28 | 2279.86 MW | 3407.78 MW | 8.07% | 12.06% |
| Oct. 29 | 2485.30 MW | 3739.03 MW | 8.79% | 13.23% |
| Oct. 30 | 862.99 MW | 1401.49 MW | 3.05% | 4.96% |
Solar Forecast
Even though solar generation is less volatile than wind generation, sudden shifts did occur again by the end of October in ERCOT. On Oct. 23, ERCOT solar peaked at 19,772 MW and stayed above 15,000 MW during daylight hours. However, the next day, it remained below 12,000 MW for the entire day, barely surpassing 10,000 MW for a few hours. Our day-ahead solar forecast accurately anticipated these changes, outperforming ISO’s forecast.
| DATE | MAE ENVERUS DAY AHEAD | MAE ISO DAY AHEAD | CAP_MAPE ENVERUS DAY AHEAD* | CAP_MAPE ISO DAY AHEAD* |
| Oct. 23 | 508.93 MW | 883.32 MW | 1.73% | 2.99% |
| Oct. 24 | 813.22 MW | 1030.16 MW | 2.76% | 3.49% |
Conclusion
Accurate ERCOT load and renewable forecast solutions are essential for traders and market participants navigating power market volatility. October’s variability in load and renewable generation underscores the importance of reliable short-term and day-ahead forecasting tools that help participants anticipate changes and make informed decisions. With decades of experience, Enverus continues to provide trusted grid analytics and forecasting solutions, enabling power traders and market participants to navigate uncertainty with confidence and precision.
About Enverus Power and Renewables Grid Analytics and Forecasting Solutions
With a 15-year head start in renewables and grid intelligence, real-time grid optimization to the node, and unparalleled expertise in load forecasting that has outperformed the ISO forecasts, Enverus Power and Renewables is uniquely positioned to support all power insight needs and data driven decision making. More than 6,000 businesses, including 1,000+ in electric power markets, rely on our solutions daily.
*CAP_MAPE represents the Mean Absolute Error (MAE) scaled by the maximum observed value (CAP) in the dataset. This scaling produces a relative error measure, ensuring that the system’s overall scale and the magnitude of values during the evaluation period do not distort the error metric. Formally,
