This week is was able to initialize my codebase for experimenting with ML forecasting, and I helped my group with narrowing down our design plans for the presentation next week.
In order to inform our decisions about what training and testing methods will be most appropriate, I devoted the first half of this week to understanding the limitations of the data we have available to us. I figured out how to efficiently access wind, solar, and general weather data and then used the wind data to produce baseline results with a persistence algorithm (predicting the previous value as the next value). This allowed me to output an array of error metrics for a naive forecasting approach that I can use to revise our goals for our models and have as a benchmark for later results. After my experiments and further research into appropriate ML techniques, I spent the latter part of the week developing design plans for the forecasting portion of the project and updating our presentation slides accordingly.
I’d say we are more than on schedule with our progress so far and after we have our design fully nailed down, progress on the codebase should come quickly. Next week, I plan to generate baseline results for our solar data and load data before starting to experiment with some slightly more effective regression models (linear regression, random forest regression). We will also devote time to our design report, taking into account feedback from our design presentation.