Combined Forecasting Model Based on BP Improved by Particle Swarm Optimization and its Application

Abstract

The BP neural network as the traditional prediction method has certain advantages, but it has some drawbacks, Such as slow convergence and sensitive to the initial weights, etc. The PSO algorithm is introduced into the neural network training, using the particle swarm algorithm to optimize the neural network weights and threshold. Through the establishment of the particle swarm - BP neural network model for power load budget, it improves the accuracy and stability of the forecast.

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

“…Theoretical investigation on a combined model for medium and long-term load forecasting based on load decomposition and big data technologies [27]. Combining a predictive model based on Back Propagation (BP) powered by PSO with its application was tackled [28], and a BP neural network based on a gray forecast model and a Markov chain was used to forecast China's load demand [29]. Application of neural network and fuzzy theory in STLF [30], optimization of electrical load forecasting for SVM model based on data mining and Lyapunov exponent was described [31], SVM forecasting method improved by chaotic PSO and its application [32], combined RS-SVM forecasting model is applied in power supply demand [33].…”