Güç tüketimi tahmini için yapay sinir ağları algoritmalarının matlab uygulaması

At the same time as there is a drive to increase electrification efforts on a global scale, new technological innovations are leading to an increase in the amount of power that is consumed worldwide. As a result, we should anticipate an increase in the amount of electricity used inside the residential setting. It is becoming increasingly difficult to generate reliable projections about future power consumption as more information about the usage of energy throughout the world becomes available. Both the buyer and the seller stand to benefit from an accurate prognosis. A customer may be able to save money and reduce their carbon footprint with the assistance of a power forecast. It is essential for the effectiveness of the provider's attempts to regulate the flow of goods that they have a forecast that can be relied upon. Consequently, this type of modeling can make a contribution to the overall optimization of the supply chain in the residential electricity industry. As a result of the extensive interest in this issue, a broad variety of techniques to solving it have been investigated and assessed. In this study, we introduce the AAA-ANN and ANN-PSO algorithms for accurately predicting energy consumption. Both of these methods are based on artificial neural networks. In order to provide accurate projections of future power consumption, the algorithms underlying both approaches were developed in Matlab and then trained using data taken from the UCP database. The AAA-ANN method outperforms other approaches in terms of error curve analysis, training accuracy rate analysis, vitesting accuracy rate analysis, training error rate analysis, and testing error rate analysis. Afterc comparing AAA-ANN to ANN and ANN-PSO, it became abundantly evident that this particular method was the best approach for forecasting future power use.