Short term load forecasting using LSTM ensembled network on utility scale load demand.

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.