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license: mit |
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language: |
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- en |
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inference: false |
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datasets: |
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- scalytics/smartmeterdata |
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**Owner:** Scalytics, Inc. |
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**Blogpost:** [Democratizing neuronal networks to predict energy consumption](https://www.databloom.ai/blog/democratizing-neuronal-networks-to-predict-energy-consumption) |
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### Model Overview ### |
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LST-E [last energy] is a Long short-term 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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The first models created are a simple baseline model, used for evaluating the performance of the later on built LSTM model. The baseline model simply shifts the values by t=1. Hence, |
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there is no t=0 and each timestamp uses the value from t-1. |
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Finally, there's the 2-layer plain vanilla LSTM. After 11 epochs, I reached a loss of 10.86. The main idea here is to build a basic forecasting model for which this seems appropriate. |
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***Happy Hacking!*** |
