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Short term load forecasting using LSTM ensembled network on utility scale load demand.
  • +2
  • Fathun Karim Fattah ,
  • Pritom Mojumder ,
  • Azmol Ahmed Fuad ,
  • Mohiuddin Ahmad ,
  • Eklas hossain
Fathun Karim Fattah
Khulna University of Engineering & Technology, Khulna University of Engineering & Technology, Khulna University of Engineering & Technology

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Pritom Mojumder
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Azmol Ahmed Fuad
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Mohiuddin Ahmad
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Eklas hossain
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Abstract

This work entails producing load forecasting through lstm and lstm ensembled networks and put up a comparative picture between the two. Our work establishes that lstm ensemble learning can produce a better prediction compared to single lstm networks. We tried to quantify the improvement and assess the economic impact that it can have on the utility companies.