Online Courses

In-Depth Seminar: Energy Statistical Analysis Seminar and Workshop

Comprehensive 2.5 day on-demand workshop with Live Q&A
Available October 16th (30 day access)

How this online course works

This comprehensive Seven module program is designed to provide a solid understanding of key statistical and analytic tools used in the energy and electric power markets. Through a combination of lecture and hands-on exercises that you will complete using your own laptop, participants will learn and practice key energy applications of statistical modeling. Be armed with the tools and methods needed to properly analyze and measure data to reduce risk and increase earnings for your organization.

Learn why companies continue to be exposed to significant energy and electricity related price risk, and how risk and value are properly quantified. Energy and electricity companies worldwide depend on accurate information about the risks and opportunities facing day to day decisions. Statistical analysis is frequently misapplied, and many companies find that "a little bit of knowledge is a dangerous thing."

This seven module training is designed as a hand-on workshop. Not only will you learn about practical energy statistical techniques and tools, but you will practice building statistical models in a workshop format. A laptop is required.

What You Will Learn

  1. Correlation & regression analysis; real option analysis; the Black-Scholes option pricing model; binomial trees; GARCH Models; the measurement of energy price risk; and how to use correlation and regression analysis for maintaining a competitive edge.
  2. Workshop exercises will have you building forecast models including time series and financial engineering price models including Geometric Brownian Motion and Mean Reversion Jump Diffusion.
  3. How to minimize price risk through operational design flexibility; measure forward price volatility and adapt Value-at-Risk concepts (VaR) for the Energy Industry.
  4. Workshop exercises will have you building VaR models, calculating volatility and simulating complex energy projects.
  5. Use actual case studies to examine 1) how Monte Carlo simulation is used to value renewable energy, demand response programs and energy storage projects; 2) bench-marking techniques used for estimating the incremental cost savings of expanding existing operations; and 3) real-option value of generation assets and power purchase agreements.
  6. Actual workshop problems and case studies will look at statistical applications and tools most frequently used in the energy industry.
  7. Learn the four manage statistical metrics.

Seminar Agenda

MODULE ONE: The Basics of Deterministic vs. Probabilistic Thinking for Energy Applications

·         Basics of data science – Information from Data

·         Descriptive Statistics, Means, Standard DeviationsDistribution Shapes

·         Frequency Distributions and Confidence Intervals

·         Implications of the Empirical Rule, Transformations and Probability

·         Univariate and Multivariate Analysis

·         Hypotheses Testing

·         Testing for Equal Means and Variances

·         Control Charts

Application: Machine Leaning and Data Science

MODULE TWO: Applications in Calculating Value at Risk (VaR)

·         The Linear Method

·         The Quadratic Method

·         Historic Simulation Method

·         Monte Carlo Method

Exercise: Setting up a Monte Carlo Simulation to Evaluate Project Value and Risk

Exercise: Calculating VaR Using Three Different Methods

MODULE THREE: Applications in Hedging Energy Exposure

·         Understanding the "Greeks"

·         How and when to Hedge

·         Delta Hedging

·         Dynamic Hedging

·         Gamma Hedging

Exercise: Calculating Hedge Ratios, Constructing an Energy Hedge and a Weather Hedge

MODULE FOUR: Applications in Component Risk Analysis

·         Payoff Diagrams

·         Portfolio VaR Diagram

·         CAPM, RAROC and the Sharp Ratio

·         Calculating Load Following Supply Risk

·         Layered Hedging using Statistical Triggers

Exercise: Customer Migration Model Estimating Migration out of Standard Offer Service

Exercise: Measuring Load Following Supply Risk

Exercise: Measuring Intermittent Renewable Supply Risk

MODULE FIVE: The Energy Forecasting Toolbox

·         Correlation and Regression Analysis for Maintaining the Competitive Edge        

·         Historical Trend Analysis

·         Univariate Time Series

·         Multivariate Time Series

·         Econometric Models

·         Bayesian Estimation

·         End-Use Models

·         Engineering or Process Models

·         Optimization

·         Network Models

·         Simulation

·         Game Theory

·         Scenarios

·         Surveys

Case Study: Statistical Reports that Everyone Can Understand

Case Study: Benchmarking to Industry Standards- GTS Steel vs. KCPL

Exercise: Building Regressions and Forecasting, PDF’s, CDF’s and Payoff Diagrams

Extra Credit: R Basics, Python Basics in Energy Statistics

MODULE SIX: Fundamental Modeling Tools and Simulation

·         Monte Carlo Simulation to Forecast Price Volatility

·         Estimating Volatility and Uncertainty In Historical Prices

·         Geometric Brownian Motion and Mean Reversion

·         Curve Fitting

Exercise: Calculating Volatility

Exercise: Simulating Prices using GBM and Mean Reversion Monte Carlo Models  

Exercise: Comparing Engineering Models, Black Scholes, Monte Carlo and Excel Functions

Exercise: Using Forecasts in Monte Carlo Simulation to Calculate Risk Premium

MODULE SEVEN: Introduction to Real Options Analysis

·         Details of Option Model Implementation

·         Real Options and Net Present Value (NPV) Analysis

·         Black-Scholes, Binomial Trees, and GARCH Models

Application: Minimizing Price Risk through Operational Design Flexibility

Application: Real Option Value of Demand Response and the Smart Grid

Exercise: Valuing Combustion Turbines using Real Options

Exercise: Valuing Gas Storage using Real Options

 

Who Should Attend

Among those who will benefit from this seminar include energy and electric power executives; attorneys; government regulators; traders & trading support staff; marketing, sales, purchasing & risk management personnel; accountants & auditors; plant operators; engineers; and corporate planners. Types of companies that typically attend this program include energy producers and marketers; utilities; banks & financial houses; industrial companies; accounting, consulting & law firms; municipal utilities; government regulators and electric generators.

Prerequisites and Advance Preparation

This online training program has no prerequisites. No advance preparation is required.

Why Choose PGS?

PGS training programs are known for their clear explanations and in-depth content. Register today, and join the over 10,000 industry professionals who trust PGS with their energy training needs.

Instructor

Ken Skinner, Ph.D.
Vice President Integral Analytics Inc.

Dr. Skinner is a Vice President for Integral Analytics Inc. (IA), an energy conservation focused analytical planning software and consulting firm. IA is fully owned by Willdan, an industry-leading energy solutions provider and sustainability consultant. Dr. Skinner supports core energy risk management and sustainability services including electric grid optimization, integrated resource planning, design and implementation of integrated demand side projects and distributed energy resources. He is part of a team specializing in energy engineering, renewable generation, electric vehicle fleets and infrastructure, program management, microgrids, financing, data analysis, software development, and other fields.

Dr. Skinner has over 20 years of energy industry experience developing energy conservation and commodity supply strategies involving portfolio risk management, hedging strategies, and least-cost supply opportunities. Having worked as an energy consultant, Dr. Skinner has significant experience in economic analysis and modeling of distributed energy projects, forward energy prices, financial derivatives, and valuation of energy assets using econometric analysis, statistical methods, optimization principles, real option valuation techniques. Dr. Skinner is widely published having served as the technology columnist for Wiley Natural Gas and Electricity Journal. He is a noted speaker on energy related topics for organizations such as AESP, IAEE, ACEEE, PLMA, IEPEC, INFORMS, Infocast, EUCI, SNL Energy and PGS Energy Training.

On-Demand Access Time

The on-demand training presentation is available for a total of 30 days from the time the training program is first viewed. After 30 days, your access to the online presentation will expire. However the PDF training program slides and other materials you receive are yours to keep.

Program Pricing


2.5 day on-demand seminar with Live Q&A

$2,495 for the first attendee, $2,195 for the second attendee, and $1,895 for each attendee thereafter

Special pricing is available for groups of 10 or more. Please email or call (440) 853-1038.

Payment and Cancellations

Payment is required prior to training program access. Payment can be made by Visa, Master Card, American Express, or corporate check. Your credit card will be charged at the time of registration unless other arrangements have been made.

Cancellations for online training programs can be made before the program access instructions are sent by PGS, and cancellations will result in a credit. For more information on PGS policies regarding administrative matters and complaint resolution, please contact our offices at (440) 853-1038.
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