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Techniques | Aim | Drawback | Remarks |
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Load forecasting based on weather information for bulk power system | Improvement in forecasting accuracy | Suitable for bulk power system only | Incorporating exogenous variables, the performance of bulk and distributed power systems improves |
Residential forecasting using DRNN | Enhance the user’s comfort level by reliable electricity availability | Model complexity increases | Residential energy forecasting is possible by sharing the load data of consumers to energy regulation commission |
LSTM-RNN for residential forecasting | Improved accuracy | Increased in accuracy only for meter level forecast | Improvement in accuracy not in convergence rate |
IoT-based load forecasting | Improved operation of power system with accuracy | Large complex framework | Impact on convergence rate |
Forecasting based on big data approach | Accuracy improved for scalable models | Complex structure with less convergence | High complexity with improved accuracy |
Week ahead forecasting using deep model with denoising auto encoders | Improved accuracy | Model performance is affected with reduced data size | Convergence rate is affected, but accuracy improved with large data size |
Artificial intelligence-based load forecasting | Reduction in MSE with improved accuracy | Accuracy with high convergence rate | Sigmoid function reduced convergence rate |
Intelligent hybrid model for load forecasting | Day-ahead load forecasting | Effective management of grid operation | Reliability is improved with high model complexity |
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