Electricity consumption forecasting method based on MPSO-BP neural network model

TL;DR

This paper introduces an MPSO-BP neural network model for electricity consumption forecasting, demonstrating improved accuracy and convergence over traditional methods through simulation and real-world prediction for a Chinese mineral company.

Contribution

The paper presents a novel MPSO-BP neural network model that enhances forecasting accuracy and convergence speed compared to existing models.

Findings

The MPSO-BP model outperforms BP, PSO, and fuzzy neural networks in convergence and accuracy.

Simulation results confirm the model's effectiveness in electricity consumption prediction.

The model successfully predicts monthly electricity consumption for 2017.

Abstract

This paper deals with the problem of the electricity consumption forecasting method. An MPSO-BP (modified particle swarm optimization-back propagation) neural network model is constructed based on the history data of a mineral company of Anshan in China. The simulation showed that the convergence of the algorithm and forecasting accuracy using the obtained model are better than those of other traditional ones, such as BP, PSO, fuzzy neural network and so on. Then we predict the electricity consumption of each month in 2017 based on the MPSO-BP neural network model.