Evaluation of the Power Demand for Economic Load Dispatch Problem Using Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network

The evaluation of power demand is fundamental in the Economic Load Dispatch problem, ensuring that the generated power meets the needs of consumers reliably and efficiently in planning system operations. This paper presented two approaches using an Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Artificial Neural Network (ANN) to evaluate the power demand. The modified IEEE 57-Bus system is considered the thermal units that incorporate renewables. The ANFIS and ANN are implemented using MATLAB online version R2023b. The results show that the ANN and ANFIS techniques are suitable for evaluating power demand. A comparison of both methods indicates that ANFIS is relatively superior to the ANNs techniques, considering the coefficient of determination of the ANNs and ANFIS were equal. The accuracy of its results in terms of prediction RMSE for the ANN and ANFIS of 10.147e-05 and 5.2177e-05 for the training and 14.639e-05 and 5.2177e-05 for the testing, respectively. Finally, the prediction accuracy of the ANFIS can be observed to be higher than that of the ANN, but the ANFIS takes longer to process. ANFIS is the method that can be appropriately applied to evaluate the power demand in this research. However, it could not guarantee for other research topics that ANFIS would be better than ANN for the RMSE. It depends on input and output data complexity and the training function type.