Here, we present the code for performing a short-term load forecasting with the help of LSTM. The data for training and testing is taken from the UCI ML website ( The code for STLF has been adapted from GitHub (

  1. We import the necessary modules and set random seeds, shown as follows:
import timefrom keras.layers import LSTMfrom keras.layers import Activation, Dense, Dropoutfrom keras.models import Sequential, load_modelfrom numpy.random import seedfrom tensorflow import set_random_seedset_random_seed(2) # seed random numbers for Tensorflow backendseed(1234) # seed random numbers ...

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