How to time series forecast with multiple time series data sets on the same time series index

How does time series work with multiple time series data sets on the same index?

For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for the same time period, daily from January 2018 - December 2018.

If I wanted to train a model that could predict the daily t+1 energy usage based on data from January 2018 to time t $$\forall t > 12/31/2018$$, how would I accomplish this?

How would I format my input data matrix for the various time series forecasting models or neural networks like LSTM or RNNs?

House 1: 50kwh ...

House 2: 15kwh ...

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To be clear, I am not asking how to predict the aggregated sum total of energy usage. I am interested in predicting the next day usage of each house.

Would I need to have two separate models, or could the weights of one single model accomplish this task? If the latter, how do I format my dataframe?