bimlp model based on deep learning for predicting electrical load demand

The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. however, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. to address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. in this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (mlp) regression model has the best performance in predicting electricity demand. furthermore, the proposed system, bimlp (bagging-improved mlp), is designed to iteratively improve its parameters using a binary search algorithm and reduce the learning error using bagging, a technique for ensemble learning. the proposed system was applied to the electric power consumption data set and achieved a value of 0.9734 in the r2 criterion. the results of implementing and evaluating the proposed system demonstrate its satisfactory performance compared to existing techniques.