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Actual vs Predicted load for July 2,2014 with LSTM and LSTM ensembled approach

Actual vs Predicted load for July 2,2014 with LSTM and LSTM ensembled approach

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Fig. 1: Proposed LSTM ensembled network with different activation...
Fig. 1: Distribution of load demand across the whole dataset with the...
Fig. 2: Generalized Block Diagram of the Work
Fig. 2: Proposed LSTM ensembled network with different activation...
+10 Fig. 3: Training Loss Comparison for LSTM and LSTM Ensembled Network
Short term load forecasting using LSTM ensembled network on utility scale load demand.
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Full-text available
  • Dec 2021
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...
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... to assess the performance of the models. The objective was to observe the contrast between the forecast performances of both of the models. With this purpose in mind, the actual load curve and the forecast curve had been mapped and juxtaposed in order to get a better comparative picture of both of the models' performances. The curves displayed in Fig. 6 exhibit a significant accuracy level for both of the models. LSTM ensembled approach showed a better correspondence for the actual vs. predicted load. Nevertheless, there were slight deviations in both of the models' curve patterns, especially in the transitional regions and sudden bends. The deviations were mostly observed in the peak ...

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Fig. 1: Distribution of load demand across the whole dataset with the...
Fig. 2: Proposed LSTM ensembled network with different activation...
Fig. 4: Training Loss Comparison for LSTM and LSTM Ensembled Network
Fig. 6: Actual vs Predicted load for July 2,2014 with LSTM and LSTM...
+2 Fig. 8: Comparison of training loss between LSTM and LSTM ensemble...
Short term load forecasting using LSTM ensembled network on utility scale load demand
Preprint
Full-text available
  • Sep 2022
p>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 utili...
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