This week I focused on properly structuring my codebase for ML forecasting and developing a pipeline for our three training datasets (load, solar, wind) centered around a simple lstm model. Code refactoring involved modularizing my data preprocessing, model training, predicting, and writing outputs to files. This made it much easier to share functions across python scripts so I could easily extend my LSTM training code.
The current LSTM model is performing well for the wind and solar data NRMSE of 11.3% and 11.0% respectively. The results for the load data, however, are not yet good enough and it appears to be resulting from overfitting, which I plan to fix this weekend.
Overall I’m making good progress, but I’m a bit behind considering I planned to begin integrating my models with the optimization and web app this week. However, we plan to put together our components beginning next week and I expect to have all my models outputting relatively accurate predictions in time for the demo.