STLF (Short-Term Load Forecasting)

Information

• STLF (Short-Term Load Forecasting) is a process of predicting the future electricity demand in the near future, typically from a few hours to a few days ahead, at a fine temporal and spatial resolution.
• STLF is essential for ensuring the reliable and economic operation of the power grid, by enabling the utilities and system operators to plan and dispatch the generation, transmission, and distribution resources in advance and maintain the balance between the supply and demand.
• STLF relies on various data sources, such as historical load data, weather forecasts, calendar and holiday schedules, and other exogenous variables that affect the electricity demand, such as temperature, humidity, wind speed, and economic indicators.
• STLF uses various statistical and machine learning techniques to model the complex and nonlinear relationship between the historical data and the future demand, such as regression, time series analysis, neural networks, support vector machines, decision trees, and ensemble methods.
• STLF models are evaluated using various performance metrics, such as mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R-squared), and correlation coefficient, and validated using various techniques, such as cross-validation, holdout testing, and backtesting.
• STLF faces several challenges, such as the uncertainty and volatility of the exogenous variables, the heterogeneity and non-stationarity of the load data, the high variability and unpredictability of the demand patterns, and the trade-off between the accuracy and computational complexity of the models.
• STLF has various applications in the energy industry, such as load forecasting for day-ahead and real-time markets, load shedding and demand response programs, renewable integration and curtailment management, outage planning and management, and energy storage and capacity planning.