Load Forecasting

Demand forecasting typically refers to predicting customer desire for electricity, while load forecasting predicts actual power consumption measured at grid points. The two are related but not identical: demand might spike due to a heat wave, but actual load depends on how many customers have air conditioning and how efficiently they use it. For practical purposes, utilities use the terms interchangeably.

Short-term forecasts (1-7 days) rely on recent demand patterns and accurate weather predictions, both of which are well-known. Long-term forecasts must account for structural changes in the economy, technology, and consumer behavior that are inherently unpredictable. Weather forecasts degrade after 10 days, and economic growth rates are uncertain beyond a few years.

Machine learning models capture seasonality through lagged features (previous year's demand at the same time), calendar features (month, day of week), or explicit seasonal decomposition before training. Some methods (Prophet, SARIMA) explicitly model trend and seasonal components. Ensemble methods combine seasonal and non-seasonal models to capture both patterns.

No; it shifts the focus from total system load to net load (load minus solar generation). With high solar penetration, net load becomes more volatile and harder to forecast due to cloud cover variability. Accurate net load forecasts become even more critical for balancing variable renewable generation.