Lastprognoser

Lastprognoser, or load forecasts, are projections of future electricity demand used by power system operators, utilities, and energy markets to schedule generation, maintain reliability, and price electricity. Forecast horizons range from minutes to months, with short-term forecasts (hour-ahead to day-ahead) guiding balancing and unit commitment, and longer horizons informing capacity planning and investment decisions.

Inputs include historical load, weather variables (temperature, wind, solar radiation), calendar effects (weekday patterns, holidays), and economic indicators. Forecasting methods combine statistical time-series models (ARIMA, exponential smoothing), regression-based approaches, and machine learning techniques (gradient boosting, neural networks). Many implementations use ensembles to improve accuracy and to produce probabilistic forecasts that express uncertainty.

The forecasting process typically involves data collection and cleaning, feature engineering, model training and validation, forecast generation, and dissemination to grid operators, utilities, and market participants. Forecasts are provided as point estimates with prediction intervals or probabilistic distributions.

Applications span grid operation and planning, including unit commitment, economic dispatch, balancing services, demand response, transmission planning, and energy market clearing. In systems with high penetration of renewables or distributed energy resources, forecast accuracy and timeliness are increasingly critical.

Challenges include data quality and resolution, model drift from changing usage patterns, integration of distributed resources, extreme weather events, and regulatory or cyber-security concerns. Ongoing research emphasizes probabilistic forecasting, uncertainty quantification, and the integration of weather and socio-economic data to improve reliability.

See also: energy forecasting, weather normalization, power system operation.