initial import

README.md

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 license: cc-by-nc-4.0
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+### AI Energy Forecast using LTSM 
+
+It basically takes some smartmeter data (5 cols, > 12mil. instances, cols: id, device_name, property, value, timestamp) and creates a custom forecast based on selected window. 
+The file is available in .py and .ipynb format, so you can choose according to your preferences.
+
+Please notice that once you load up the smartmeter data, there are inputs created on the timestamp col like wd_input (the weekday of the timestamp), as well as a cos(inus) and sin(us)
+time inputs, giving the model the ability to keep track of the daytime of each instance. Finally, the inputs are merged to an input df, standardized and differenced.
+After that, some functions are used to give the user the ability to use time windows from the data. Based on these, the model generates forecasts.   
+   
+![Model](https://github.com/databloom-ai/LLM-LTSM/blob/main/energy-forcast/model.png?raw=true)
+   
+The first models created are a simple baseline model, used for evaluating the performance of the later on built LTSM model. The baseline model simply shifts the values by t=1. Hence,
+there is no t=0 and each timestamp uses the value from t-1.
+Finally, there's the 2-layer plain vanilla LTSM. After 11 epochs, I reached a loss of 10.86 which is rather mediocre. However, the main idea here is to build a basic forecasting model
+for which this seems appropriate.   
+
+
+![LTSM](https://github.com/databloom-ai/LLM-LTSM/blob/main/energy-forcast/LTSM.png?raw=true)