The Electricity Consumption Prediction using Hybrid Red Kite Optimization Algorithm with Multi-Layer Perceptron Neural Network

Since the electricity consumption’s prediction is one of the most important aspects of energy manage ment in each country, various methods based on artificial intelligence have been proposed to manage it. One of these methods is Artificial Neural Networks (ANN). To improve the performance of ANNs, an efficient algorithm is necessary to train it. Back Propagation (BP) algorithm is the most common algorithm employed in training ANNs, which is based on gradient descent. Since BP may fall in local optima, it cannot provide a good solution in some problems. To overcome this shortcoming, optimiz ation algorithms like meta-heuristic algorithms can be applied to train ANNs. In this study, a new meta-heuristic algorithm called Red Kite Optimization Algorithm (ROA) is introduced, which is inspired by the social life of red kites in nature. The ROA has several advantages such as simplicity in structure and implementation, having few parameters and good convergence rate. The perfprmance of ROA is compared with some recent metaheuristic algorithms on benchmark functions of CEC2018. Also, it is employed to train Multi-Layer Perceptron (MLP) for the electricity consumption prediction at peak load times in Iran. The results show the good performance of proposed algorithm compared with competitor algorithms in terms of solution accuracy and convergence speed.