Utility Analytics Fundamentals
Techniques, Applications and Case Studies
January 23-24, 2018
Denver, CO

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Analytics — the scientific process of transforming data into insight for making better decisions — are used in virtually every division of a utility organization.  During the past few years this knowledge requirement in the industry has emerged and accelerated.  It is now a “must-have” skill for almost all utility professionals: from planning, to operations, to trading, to mid-level management, to the C-suite, and everywhere in between.  The deployment of various sensors and advanced meters has brought a large amount of data to the industry.  Increased computing power has made quantitative analytics plausible, timely and economically feasible.  Meanwhile, the advancement of information technologies has enabled utilities to make real-time operational decisions that are fact-based and data-driven.   A challenge, though, has been to compile a coherent approach for professionals not having advanced quantitative training within the utility to leverage the multiple applications of analytics so as to achieve better key performance indicators (KPIs).

This one-and-a-half-day course provides an introduction to analytics in the context of the electric power systems and industry. The course is designed for engineers, planners, analysts and managers who are either new to the utility industry or looking to develop a better understanding of how to apply analytics across the entire organization. Through 10 diverse case studies and 4 hands-on exercises, attendees will gain a fundamental understanding of how to apply analytics within the utility industry, the classical and emerging problems, and how to tackle those problems using the quantitative techniques in the enterprise environment.

Learning Outcomes

Employing case studies and workshop exercises, this instructional course will demonstrate in easy-to-follow methodologies the applications of analytics most frequently used in the utility industry.  It will:

  • Define the basics of descriptive, predictive and prescriptive analytics and when the appropriate tool(s) should be applied
  • Demonstrate how to use numerical and graphical to describe data
  • Discuss how to manage data-intensive, analytical projects
  • Indicate how to develop, build and coordinate a team to tackle analytical challenges at different hierarchical levels of the organization
  • Provide the analytics foundations used to derive the following key utility functions
    • Electricity demand forecasting
    • Reliability planning
    • Demand response program management
    • Unit commitment
    • Energy trading
    • Forecasting emerging technologies
      • Solar rooftop penetration
      • Electric vehicles adoption
    • Outage management and storm restoration
    • Aging infrastructure and asset management



EUCI has been accredited as an Authorized Provider by the International Association for Continuing Education and Training (IACET).  In obtaining this accreditation, EUCI has demonstrated that it  complies with the ANSI/IACET Standard which is recognized internationally as a standard of good practice. As a result of their Authorized Provider status, EUCI is authorized to offer IACET CEUs for its programs that qualify under the ANSI/IACET Standard.

EUCI is authorized by IACET to offer 0.9 CEUs for this event.

Requirements for Successful Completion of Program

Participants must sign in/out each day and be in attendance for the entirety of the course to be eligible for any continuing education credit.

Instructional Methods

PowerPoint presentations, case studies, and workshop exercises will be used in this program.


Tuesday, January 23, 2018

8:00 – 8:30 a.m. :: Registration and Continental Breakfast                              

8:30 – 8:45 a.m. :: Introductions and Overview

8:45 – 9:30 a.m. :: Evolution of Analytics Practice in the Electric Power Industry      

  • History of the industry
  • Electric power systems
  • Advancement in analytics and the relationship to IT

9:30 – 10:15 a.m. :: Introduction to Analytics     

  • Case study 1: analyzing utility stocks
  • Descriptive analytics
  • Predictive analytics
  • Prescriptive analytics
  • Hands-on exercise

10:15 – 10:30 a.m. :: Morning Break                      

10:30 a.m. – 12:00 p.m. :: Descriptive Analytics                                              

  • Case study 2: describing electricity demand
  • Numerical methods
  • Graphical methods
  • Hands-on exercise

12:00 – 1:00 p.m. :: Group Luncheon                                                                               

1:00 – 2:30 p.m. :: Predictive Analytics                                                              

  • Case study 3: peak demand forecasting
  • Exponential smoothing
  • Regression analysis
  • Hands-on exercise

2:30 – 3:00 p.m. :: Networking Break                                                                

3:00 – 4:45 p.m. :: Predictive Analytics (Continuation)                       

  • Case study 4: power distribution system reliability
  • Simulation
  • Case study 5: managing demand response programs
  • Survival analysis

5:00 p.m. :: Course Adjourns for the Day

Wednesday, January 24, 2018

8:00 – 8:30 a.m. :: Continental Breakfast                                                                      

8:30 – 10:00 a.m. :: Prescriptive Analytics                                               

  • Case study 6: unit commitment
  • Linear programming
  • Dynamic programming
  • Hands-on exercise
  • Case study 7: energy trading
  • Electricity market and locational marginal pricing
  • Risk analysis

10:00 – 10: 15 a.m. :: Morning Break                                                                      

10:15 – 11:00 a.m. :: Comprehensive Case Studies                                   

  • Case study 8: Forecasting solar rooftop penetration
  • Case study 9: Outage analytics
  • Case study 10: Aging infrastructure and asset management

11:00 – 11:45 a.m. :: Utility Analytics in the Enterprise Environment 

  • Analytics solutions and services
  • Managing analytics projects
  • Cross-functional analytics team
  • Career path and career development

11:45 a.m. ::  Program Concludes  


Tao Hong, Director of BigDEAL (Big Data Energy Analytics Laboratory), University of North Carolina at Charlotte

Dr. Tao Hong is the Director of BigDEAL (Big Data Energy Analytics Laboratory) at University of North Carolina at Charlotte and Chief Data Scientist of Hong Analytics. He has been providing training and consulting services to more than 100 organizations in the energy industry worldwide. He is the Founding Chair of IEEE Working Group on Energy Forecasting, General Chair of Global Energy Forecasting Competition, lead author of the online book Electric Load Forecasting: Fundamentals and Best Practices, and author of the blog Energy Forecasting. Dr. Hong received his B.Eng. in Automation from Tsinghua University in Beijing and his PhD with co-majors in Operations Research and Electrical Engineering from North Carolina State University.


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