Time series forecasting in year intervals

I am pretty new to ML and creating my own prediction models. My case involves a lot of data that continues to grow every hour such as solar data that has yearly/monthly/daily trends. There are many different cases for the data that can vary from system to system and I want to be able to create a model that can use the years worth of data in the past and use that model to predict the estimated solar production for any given time range by learning trends over the previous years.

For example If I have data for a solar system from May 25, 2021 - Today and want to predict what the production could look like in November of 2023 I would want to train the model on the existing data and input my predicted time range as November 10, 2023 - November 12, 2023.

I have so far tried to use the tutorial for time series forecasting but that makes it pretty hard when it’s just a continuation of data going forwards. I also used an RNN model which to me seemed like the best idea at the time.

Is there any good tutorials on my case or something I should read into to get started on this kind of prediction method?