I have a fairly simple dataset of energy consumption values generated every half hour. I want to train a model to predict the energy consumption at a particular time.
How do I model time values?
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At first sight, the total acumulated energy consumption seems to have a linear relation with time, so I suggest to try a linear regression at first. There are several libraries you can use to code it. I recommend you do it with pandas and sklearn, here is an answer related to this: answer.
If the relation is not linear, so I could try with a more complex model (but I suggest to keep simplicity at first). Since you are trying to predict a temporal serie, I would try with an LSTM model. Here is a tutorial to implement an LSTM neural network with keras.
When you are a hammer, every problem looks like a nail.
This is a textbook problem in time-series analysis, and has been engaged with some decent levels of success using things like auto-regressive methods (ARIMA...) since the 1970's era.
How you handle your data depends on the nature of the data. Mileage varies. There is no silver bullet.
Here are some examples where variations of it are engaged.