WITHDRAWN: Hybrid deep learning approach for load forecasting in power systems

Abstract The load forecasting is one of the important features in the electricity market. The traditional mechanisms are not sufficient to improve the accuracy in the load predictions. To overcome this issue, a hybrid model has been developed to load forecast for a week in the electricity market. This model takes the inputs of ensemble forecasting and combines the architectures of deep learning and artificial neural networks. The proposed model follows the three layer mechanism. In the first layer, it clusters the input based on the fuzzy rules for creating the ensemble predictions. In the second layer, the regression method is applied for each cluster to develop load forecasting issue. In the third layer, the averaging of the Radial Basis Function convolution neural network regression ensemble predictions will be performed. The APSPDCL data set is considered as the case study and the proposed model predicts the hourly load for the next one week.

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