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.

Out of Sample Tests

• Error analysis.
• Cross validation.
• Sliding simulation.

Weather Station Selection

• Two fundamental questions.
• A common method.
• Unconstrained weather station selection.
• Seven-step implementation.

Outlier Detection and Data Cleansing

• Definitions of outlier.
• Three examples.
• Hidden outlier.
• A modeling approach to outlier detection and data cleansing.

More about Recency Effect

• 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.

Combining Forecasts

• Motivation.
• 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.