Integration of particle swarm optimization and neural network to calculate the cost of electricity distribution

Bagheri Shadbad, Mohammad Taghi; Zeynali, Mehdi; Ebrahimi Dishabi, Mohammad Reza

doi:10.22075/ijnaa.2024.32923.4895

Integration of particle swarm optimization and neural network to calculate the cost of electricity distribution

Articles in Press

Document Type : Research Paper

Authors

Mohammad Taghi Bagheri Shadbad ¹ ^¶

Mehdi Zeynali ² ^¶

Mohammad Reza Ebrahimi Dishabi ¹

¹ Department of Computer, Miyaneh Branch, Islamic Azad University, Miyaneh, Iran

² Department of Accounting, Tabriz Branch, Islamic Azad University, Tabriz, Iran

10.22075/ijnaa.2024.32923.4895

Abstract

In today's competitive economy, issues related to electrical energy, including forecasting, optimization, and storage, are of paramount importance. Therefore, electricity and price forecasting are considered as the main planning and decision-making factors for future development projects and the operation of power systems. In fact, with the growing trend of privatization and competitiveness of the electricity market in the world, the main goal of electricity distribution companies is to increase their income and reduce investment and operating costs, the main components of which are energy costs. Is an effective way to accurately calculate the cost of electricity distribution. In this paper, by combining the particle optimization algorithm and neural network, a new and efficient method for calculating the cost of electricity distribution network is presented. In the proposed method, first the initial weights of the neural network are 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 presented in this paper, its performance was evaluated, and the accuracy of the proposed method was compared with previous works; the simulation results showed that the proposed method has a higher accuracy than previous works.

Keywords

Electricity

Electricity Distribution

Forecasting

Data Mining

Particle Swarm Optimization Algorithm

Neural Networks

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