This is a case study of how MATLAB can be used to forecast short-term electricity loads for the Australian market using Sydney temperature and NSW histroical load data sets. Nonlinear regression and neural network modeling techniques are used to demonstrate accurate modeling using historical, seasonal, day-of-the week, and temperature data.
• Forecasting short-term electricity loads and prices
• Accessing data from regional wholesale electricity markets
• “White-box” modeling using customisable algorithms and viewable-source functions
• Automatic Report Publishing
This case study 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.
David Willingham (2022). Electricity Load Forecasting for the Australian Market Case Study (https://www.mathworks.com/matlabcentral/fileexchange/31877-electricity-load-forecasting-for-the-australian-market-case-study), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
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