A Combination Model of Artificial Bee Colony and Multilayer Perceptron Neural Network to Calculate the Cost of Electricity Distribution

Abstract

After the restructuring of the electricity market and turning it into a competitive market where the price of electricity is determined by the forces governing the market, the cost and supply and demand, price fluctuations in this market have increased. Today, issues related to predicting the cost of electricity are of great importance, and several methods have been proposed to predict it. Since the electricity market price model usually has complex features such as instability, nonlinear conditions and high fluctuations, mathematical and traditional methods are usually not suitable for solving these problems. As a result, artificial intelligence methods have higher accuracy in predicting the full price. Are in power distribution networks. In this paper, by integrating the artificial bee colony optimization algorithm and neural network, a new and efficient method for calculating and predicting the cost of electricity distribution network is proposed. In the proposed method, first the initial weighting of the neural network weights is determined using the particle optimization algorithm and then in the next step, based on the perceptron algorithm, the final weights of the neural network are updated to calculate the cost of electricity distribution. After implementing the prediction model developed in this paper, its performance was evaluated and the accuracy of the proposed method was compared with previous works, and the simulation results showed that the proposed method has a higher accuracy than previous works.