Train_test_split for time series forecasting
Not sure if you are looking for the train_test_split, or the model input?
If it's the first, I suggest you use all data. Use the first 3/4 for training and the rest for testing. Also you might want to use rolling windows.
On the other hand, if you are looking for the model input, the answer is highly dependent on your data set. I can put some assumptions out there that may help you.

A solar power time series can be expected to have a very strong daily seasonality - in 15min periods you will see that nicely.

Depending on the location you might also see a yearly seasonality, e.g. lower power generation in winter.

I would not expect to see a weekly, monthly, or other seasonalities.

Since your time series is only 1y3m you will also most likely not see a general trend in the power generation.


Thus, your model should address these two seasonalities: daily and yearly. I would expect the daily power generation to have a strong autocorrelation (weather tomorrow is most likely same as today). Therefore, you might not need a very long history for that. Perhaps only one or two months to forecast the following month. However, if you have a strong yearly seasonality you might need longer training data to capture the rising and falling trend correctly.
I'm doing solar panel power generation forecast using 1 year and 3 months data and I want to forecast 1 month of this. The data is in 15 minutes periods.
My question is, if I want to make a monthly forecast how many train should I use to get a good prediction? The last 3 months, 6 months or all the data?
And for testing? How many months or weeks should I take?
Thanks and any insight would be appreciated.