Business Knowledge Series course

Presented by Tao Hong, Ph.D., Assistant Professor and NCEMC Faculty Fellow, University of North Carolina, Charlotte

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

Formats available	Standard duration
Classroom:	1.0 day

Before attending this course, you must attend the Electric Load Forecasting: Fundamentals and Best Practices course.

This course addresses SAS/ETS, SAS/STAT software.

• error analysis
• cross validation
• sliding simulation

• two fundamental questions
• a common method
• unconstrained weather station selection
• seven-step implementation

• definitions of outlier
• three examples
• hidden outlier
• a modeling approach to outlier detection and data cleansing

• how 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

• motivation
• forecast combination methods
• practical considerations

• cross validation
• weather station selection
• outlier detection and data cleansing
• recency effect modeling
• forecast combination

• grouping and clustering methods
• ARIMA models for electric load forecasting
• probabilistic electric load forecasting
• other emerging topics