Prediction of electricity-consumption and residential bills based on artificial neural network

Nowadays, predicting the value of electrical usage has made it easier for electricity consumers to reduce their residential bills. This is done by introducing a new prediction method based on the design and foundation of artificial neural network (P-EANN) technology, which is a branch of intelligent machine learning (ML) technology. The P-EANN method is based on actual data of actual power quantities that can be measured by electricity meters for the electrical model and is compared with training data that is predicted and set to the electrical usage for comparison with the reading needed to reduce residential bills. From the root mean square error (RMSE), we can find the accuracy of the residential bills ($) in the P-EANN method, which is equal to 35.69%, and the accuracy of the residential bills ($) in the standard method, which is equal to 0.00%. then the results of the MATLAB simulation for the P-EANN method enhance and reduce the residential bills from 0.5 to 4.5 dollars per day. Thus, the problem of excessive electrical usage is solved, and consumers know how to consume energy well in any place.