This week was focused on integrating the components of our project in preparation for the demo next week. For my forecasting models, this meant I had to ensure that my trained models were outputting consistent and reasonable predictions given current weather forecasts. To make this easier, i created a main file with three functions (one for each of solar, load, and wind forecasting) with each function taking in a location and returning the respective predictions in a pandas dataframe, indexed by hour. This way the web app and the optimization solver and access the forecasts from one place.
For next week, I’m looking to seriously improve the performance of my models, since the load and wind predictions seem to be especially overfitting. One idea that I’ve started experimenting with is using an autoML tool which I’ve been helping develop as part of my research at the Auton Lab. I’ve already run our wind data through this autoML system and identified some models that achieve better results than our current LSTM. A ranking of these models by test R^2 score can be found at the bottom of this post.
