Energy Analytics

This course is offered both online and on campus. People outside UNC Charlotte can register the course as Post-Baccalaureate (Non-Degree) Students.

Course Description

This course is designed for current and future analysts, operators, planners and their managers in the energy industry. It covers major energy related applications of Descriptive, Predictive and Prescriptive Analytics. The topics include energy data analysis, load forecasting, price forecasting, renewable generation forecasting, energy trading and risk management, demand response and customer analytics, and utilities outage analytics.
Tao Hong and David A. Dickey, “Electric load forecasting: fundamentals and best practices”, OTexts (available online:
Rob J. Hyndman and George Athanasopoulos, “Forecasting: Principles and Practice”, OTexts (available online:
Supplementary materials:
  1. Carl D. Meyer (2000) Matrix Analysis and Applied Linear Algebra. SIAM.
  2. R. Lyman Ott and Michael T. Longnecker (2008) An Introduction to Statistical Methods and Data Analysis. Cengage Learning.
  3. Michael Kutner, Christopher Nachtsheim, John Neter and William Li (2004) Applied Linear Statistical Models. McGraw-Hill/Irwin.
  4. William W. S. Wei (2005) Time Series Analysis: Univariate and Multivariate Methods. Addison Wesley.
  5. George Box, Gwilym M. Jenkins, and Gregory Reinsel (1994) Time Series Analysis: Forecasting and Control. Prentice Hall.
  6. John C. Brocklebank, and David A. Dickey (2003) SAS for Forecasting Time Series. SAS Publishing.
  7. H. Lee Willis (2002). Spatial Electric Load Forecasting. CRC Press.
  8. Tao Hong (2008). Long Term Spatial Load Forecasting. M.S. thesis, NCSU.
  9. Rafal Weron (2006). Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach. Wiley.
  10. Tao Hong (2010). Short Term Electric Load Forecasting. Ph.D. dissertation, NCSU.
  11. James W. Taylor, Lilian M. de Menezes, Patrick E. McSharry (2006). A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting.
  12. Shu Fan and Rob J. Hyndman (2012). Short-term load forecasting based on a semi-parametric additive model. IEEE Transactions on Power Systems.
  13. Shu Fan, K. Methaprayoon, and Wei-Jen Lee (2009). Multiregion load forecasting for system with large geographical area. IEEE Transactions on Industry Applications.
  14. H. S. Hippert, C. E. Pedriera and R. C. Souza (2002). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems.
  15. Bo-Juen Chen, Ming-Wei Chang and Chih-Jen Lin (2004). Load forecasting using support vector Machines: a study on EUNITE competition 2001. IEEE Transactions on Power Systems
  16. Tao Hong, Jason Wilson and Jingrui Xie (2014). Long term probabilistic load forecasting with hourly information. IEEE Transactions on Smart Grid.
  17. Rob J. Hyndman and Shu Fan (2010). Density forecasting for long-term peak electricity demand. IEEE Transactions on Power Systems.
  18. Pierre Pinson, Christophe Chevallier, George N Kariniotakis (2007). Trading wind generation from short-term probabilistic forecasts of wind power. IEEE Transactions on Power Systems
  19. C Monteiro, R Bessa, V Miranda, A Botterud, J Wang, G Conzelmann (2009). Wind power forecasting: state-of-the-art 2009. Argonne National Laboratory.
  20. Tao Hong, Shu Fan and Pierre Pinson (2014). Global Energy Forecasting Competition 2012. International Journal of Forecasting.
  21. Morales, J.M., Conejo, A.J., Madsen, H., Pinson, P., Zugno, M. (2014), "Integrating Renewables in Electricity Markets", Springer.
Lecture notes and other reading materials will be provided through the course website on Moodle2.
Learning Objectives
Upon completion of the course the students will be able to:
  1. Understand basic concepts of analytics, including descriptive analytics, predictive analytics and prescriptive analytics;
  2. Understand applications of Analytics in the energy industry;
  3. Know how to use advanced statistical or mathematical software such as SAS, R or other commercial packages for energy Analytics;
  4. Know how to apply exploratory data analysis to energy data;
  5. Know how to develop load, price, wind and solar forecasts;
  6. Know how to make trading decisions under uncertainties of energy forecasts.
Case Studies
The students will be required to perform a case study in one of the following areas: electric load forecasting, electricity price forecasting, wind power forecasting and solar power forecasting. Multiple students or teams will be assigned to each area to formulate a competition*. The grade of the the case study will be based on 1) ranking of the forecasting accuracy; 2) peer evaluation on the methodologies; 3) instructor evaluation on the peer review reports.
* In the class of Fall 2014, students will be joining the Global Energy Forecasting Competition as the case study for this course.

Course Contents & Tentative Schedule
 1 Greetings; basic concepts of analytics
 2 Introduction to energy analytics
 3 Describing and visualizing the energy data
 4 Forecasting and optimization
  Case study milestone 1
 5 Energy forecasting I: electric load forecasting
 6 Energy forecasting II: electricity price forecasting
 7 Energy forecasting III: wind power forecasting
 8 Energy forecasting IV: solar power forecasting
  Case study milestone 2
 9 Energy trading and risk management
 10 Emerging topics I: demand response and customer analytics
 11 Emerging topics II: utilities outage analytics
 12 Overview of energy analytics
  Case study milestone 3
  FINAL REPORT (Case study reports due and presentations)

This course has been awarded for IIF Student Forecasting Awards. Please refer to THIS BLOG POST for more information. 

IIF Student Forecasting Recipients

  • Jingrui Xie (2014)
  • Xiazhi Fang (2015)