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I am a beginner Python user. My weather data is made up of various variables. It consists of three months of one-minute time data, ambient environmental data (sunlight, ambient temperature, wind speed, etc.), internal environmental data (internal temperature, humidity), smart farm internal control variables (shielding screen, exhaust fan, ceiling fan, etc.), control set temperature (ventilation temperature, heating temperature), energy consumption (target).

Through these three-month data, a model that minimizes energy consumption of smart farms should be created. Thereafter, one month's worth of data is additionally provided, and there is no smart farm internal control variable in this data. My model should be used to predict these internal control variables and check the amount of heat supplied to make them as low as possible. (FYI, 1 of the internal control variables shows fan, 0 shows fan stop, and 0 to 100 shows light shielding or heat shielding, 50% open at 50 or 100% open at 100).)

I'm having a hard time solving this problem. I would appreciate it if you kindly let me know how to proceed with the analysis and related data or analysis techniques.

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Modeling any industrial process is quite complex because there are a lot of physical and non-linear events.

That's why I usually recommend simulating the most important processes first with a scientific tool like Scilab, Simulink, or Labview: https://www.scilab.org/use-cases/powerful-modeling-and-big-data-analysis-energy-transition

A simulation is interesting, not only to understand how physics between things works, but also to apply a partial or complete machine learning model to optimize energy consumption.

Finally, you can apply a machine learning model with Reinforcement Learning: https://github.com/ADGEfficiency/energy-py https://github.com/smasis001/smart-grid-peak-tariff-optimization/blob/master/notebooks/OptimizationAlgorithm.ipynb

Or gaussian: https://github.com/jaimergp/easymecp

These are just examples, there are plenty of existing energy optimization models available on GitHub.

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