Business Knowledge Series Kurs
Durchgeführt von Tao Hong, Ph.D., Assistant Professor and NCEMC Faculty Fellow, University of North Carolina, Charlotte
Verfügbare Schulungsformen / Formats available | Dauer (kann variieren, Details s. Terminliste) | | |
Präsenzkurs: |
2.0 Tage | | |
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In diesem Kurs wird mit folgenden Software Modulen gearbeitet / This course addresses SAS/ETS, SAS/STAT Software
Introduction to Electric Load Forecasting- overview of the electric power industry
- business needs of load forecasts
- driving factors of electricity consumption
- classification of load forecasts
- software applications
Salient Features of Electric Load Series- a general approach to electric load forecasting
- overview of the data pool
- trend and seasonality
- more salient features
Multiple Linear Regression- naive models
- trend
- class variables
- polynomial regression
- interaction regression
- rolling regression
A Naive Benchmark for Short-term Load Forecasting- motivation
- criterion
- a naive MLR benchmark
- applications
- two more salient features
Customizing the Benchmarking Model- recency effect
- weekend effect
- holiday effect
- case studies
- two more salient features
Very Short-Term Load Forecasting- hour ahead load forecasting
- weighted least squares regression
- dynamic regression
- two-stage method
- extensions
Medium/Long-Term Load Forecasting- macroeconomic indicator
- weather normalization
- forecasting with weather variation
- forecasting with cross scenarios
Variables, Methods, Techniques, and Further Readings- load, weather, calendar, macroeconomic indicator, etc.
- similar day and hierarchy
- regression
- ARIMA
- exponential smoothing
- support vector machine
- artificial neural networks
- fuzzy systems and fuzzy regression
- relevant and readable books
- load forecasting papers
- training courses
Frequently Made Mistakes- counterexamples
- expectation
- data
- models
- decisions
Software Applications
