I am working on an energy demand forecasting project, where I am using the Facebook Prophet model. I have used 3 years (2017-2020) of training data to forecast energy demand for a week at an hourly interval. The model performs best during summer and worst during winter.
If I want to compare and evaluate the model performance during the 2019 summer, I Have to reduce the training period (2017 - June 2019). Is this a statistically correct way to do this?
In general, if I want to see how seasonality affects the forecasts, is it fair to change the training period of the model?