How to go about cooling load forecast for a district level dataset? I am trying to build a high accuracy cooling load forecast for a district-level dataset over various time horizons. My data consists of a time series of cooling load and consecutive weather data. I added a bunch of other features such as an hour of the day, day of the week, holiday or not, etc. My question is, what are some current models for the prediction of the time series load dataset like mine? I gather LSTM is the way to go. However, given the features I have identified, are there other models that are more accurate for such a forecast? If I chose to go the NN fitting route, what are some other features I can add?
For a good overview of common statistical methods, check our Rob Hyndman's Forecasting Principles and Practices.
For a comparison of different models, including machine learning and deep learning, check out the M4 competition and their companion repo. The current state-of-the-art on the M4 data set is N-BEATS as far as I know. 
For a replication and extension of the M4 study in Python, check out sktime (disclaimer: I'm one of the developers) and our preprint.