In this webinar, you will learn how MATLAB can be used to forecast short-term electricity loads and prices. Nonlinear regression and neural network modeling techniques are used to demonstrate accurate modeling using historical, seasonal, day-of-the week, and temperature data.

Highlights include:
• Forecasting short-term electricity loads and prices
• Accessing data from regional wholesale electricity markets
• “White-box” modeling using customizable algorithms and viewable-source functions
• Deploying and integrating an energy load forecaster

This webinar is for practitioners at power generators, utilities or energy trading groups whose focus is transmission planning, distribution operations, derivative valuation, or quantitative analysis. Familiarity with MATLAB is not required.

View MATLAB code from this webinar on MATLAB Central.

NOTE: As of R2015a, the Application Deployment products referenced in this video have changed. For the details of this transition, please watch a short video on the Application Deployment R2015a Transition.

About the Presenter: Ameya Deoras is an application engineer at MathWorks with a focus on the Finance industry. Prior to joining MathWorks in 2008, Ameya undertook graduate research in computational gene prediction as well as robust speech recognition, both involving building statistical models for pattern recognition on large datasets using MATLAB. Ameya holds a B.S. in Electrical Engineering from the University of Illinois and an M.S. in Electrical Engineering from the Massachusetts Institute of Technology.