Research and Markets has announced the addition of the "Three Days: Energy Statistical Analysis Seminar and Workshop" conference to their offering.

This comprehensive three-day 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."

Agenda:

Day One:

- The Basics of Deterministic vs. Probabilistic Thinking for Energy Applications

- Basics of data science - Information from Data

- Descriptive Statistics, Means, Standard Deviations, Distribution Shapes

- Frequency Distributions and Confidence Intervals

- Implications of the Empirical Rule, Transformations and Probability

- Fundamental Modeling Tools and Simulation

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

Application: Calculating Value at Risk (VaR)

- The Linear Method and The Quadratic Method

- Historic Simulation Method

- Monte Carlo Method

Exercise: Calculating VaR Using Three Different Methods

Application: Hedging Energy Exposure

- Understanding the "Greeks"

- How and when to Hedge

- Delta Hedging

- Dynamic Hedging

- Gamma Hedging

Application: 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

Correlation and Regression Analysis for Maintaining the Competitive Edge

- Univariate and Multivariate Analysis

- Hypotheses Testing

- Testing for Equal Means and Variances

- Control Charts

Day Two:

The Energy Forecasting Toolbox

- 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

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

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

Day Three:

Introduction to Real Options Analysis

- Details of Option Model Implementation

- Real Options and Net Present Value (NPV) Analysis

- Estimating Volatility and Uncertainty In Historical Prices

- Black-Scholes, Binomial Trees, and GARCH Models

- Geometric Brownian Motion and Mean Reversion

Application: Minimizing Price Risk through Operational Design Flexibility

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

Exercise: Calculating Volatility

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

Exercise: Valuing Combustion Turbines using Real Options

Exercise: Valuing Gas Storage using Real Options

For more information about this conference visit http://www.researchandmarkets.com/research/kjpr5p/three_days