updated model card
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README.md
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license:
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### AI Energy Forecast using LTSM
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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)
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time inputs, giving the model the ability to keep track of the daytime of each instance. Finally, the inputs are merged
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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.
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![Model](https://github.com/databloom-ai/LLM-LTSM/blob/main/energy-forcast/model.png?raw=true)
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license: mit
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language:
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- en
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library_name: LTSM
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inference: false
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datasets:
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- databloom/smartmeterdata
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**Owner:** DataBloom AI, Inc.
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### Model Overview ###
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LSTEnergy [last energy] is a Long-Term-Short-Memory model to predict energy consumption forecasts based on historical data. It basically takes
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some smartmeter data (5 cols, > 12mil. instances, cols: id, device_name, property, value, timestamp) and creates a custom forecast based on selected window.
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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)
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time inputs, giving the model the ability to keep track of the daytime of each instance. Finally, the inputs are merged into an input df, standardized, and differenced.
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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.
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![Model](https://github.com/databloom-ai/LLM-LTSM/blob/main/energy-forcast/model.png?raw=true)
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