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.
Do you have video explain on how to build this code
Hi David, is a recorded webinar of this case study available? I am actually carrying out a similar study. It would be a great input to me. Thanks.
I have developed another example for long term forecasting using econometrics techniques, check it out at:
Hi has someone modified it for a long term forecasting ?
I am usinf Artificial Neural network in my work to predict a model that can be used same experiments information to predict new results with less error ratio in comparison to experimental and theoretical ones.
I have done some work and got results but the error ratio is still high.
If you can help me or you know any one how is doing this work please send me back on my email, firstname.lastname@example.org
Change to description
Added pdf of the presentation + file updates