This hands-on workshop is open to those who attended the Electric Load Forecasting: Fundamentals and Best Practices course. This course includes lecture and hands-on lab exercises that explore advanced topics in electric load forecasting.
Learn How To
Perform time series cross validation. Select weather stations. Detect outliers and cleanse data. Use comprehensive temperature information. Combine forecasts.Who Should Attend
Load/price forecasters, energy traders, quantitative/business analysts in the utility industry, power system planners, power system operators, load research analysts, and rate design analysts
Prerequisites
Before attending this course, you must attend the Electric Load Forecasting: Fundamentals and Best Practices course.
SAS Products Covered
SAS/ETS;SAS/STAT
Course Outline
Out of Sample Tests
Error analysis. Cross validation. Sliding simulation.Weather Station SelectionTwo fundamental questions. A common method. Unconstrained weather station selection. Seven-step implementation.Outlier Detection and Data CleansingDefinitions of outlier. Three examples. Hidden outlier. A modeling approach to outlier detection and data cleansing.More about Recency EffectHow many lagged temperatures can we afford? "Optimal" combination of lagged and average temperatures. Recency effect in hierarchical load forecasting. Search algorithms for recency effect modeling.Combining ForecastsMotivation. Forecast combination methods. Practical considerations.Case Studies (computer lab session)Cross validation. Weather station selection. Outlier detection and data cleansing. Recency effect modeling. Forecast combination.Emerging Topics (optional)Grouping and clustering methods. ARIMA models for electric load forecasting. Probabilistic electric load forecasting. Other emerging topics.
