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I would like to forecast the heat load of a district heating network given its past values, the temperature and the 3-day ahead forecast of the temperature with an LSTM RNN. The data is hourly and I try to forecast the sequence of the next 72 values, so 72h into the future. I was wondering which approach to chose here?

  1. Using the historical load, temperature and temperature forecast as input and forecasting the sequence of the 72h heat load into the future at once via an output layer with 72 neurons.

  2. Using the historical load and temperature forecast as input and forecasting one step into the future. Then iteratively forecasting the next value with the forecasted heat load and the temperature forecast.

That's the 2 options I have thought about so far. Are they somehow implementable or do I miss something important? Unfortunately, the literature I have found did not really suggest what method to chose on predictions for more than one-time step into the future and some kind of exogenous variables that are known in the future.

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I prefer option 1 where you will predict the next 72 hours at once with all the history and future data (like the forecast). I do not see why option 2 would outperform option 1 in theory.

And yes, it is doable. Take a look at this paper.

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This is part of the trial and error process, so you will know which method is better once you try them. Both approaches are implementable and have their pros and cons as shown in this excellent thesis on this subject.

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