Design of intelligent long-term load forecasting with fuzzy neural network and particle swarm optimization

R. Wai, Yu-Chih Huang, Yi-Chang Chen
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引用次数: 4

Abstract

In recent years, an intelligent micro-grid system composed of renewable energy sources is becoming one of the interesting research topics. The success design of long-term load forecasting (LTLF) enables the intelligent micro-grid system to manipulate an optimized loading and unloading control by measuring the electrical supply for achieving the best economical and power efficiency. In this study, intelligent forecasting structures via a similar time method with historical load change rates are developed based on the basic frameworks of fuzzy neural network (FNN) and particle swarm optimization (PSO). In the regulative aspect of network parameters, conventional back-propagation (BP) and PSO tuning algorithms are used, and varied learning rates are designed in the sense of discrete-time Lyapunov stability theory. The performance comparisons of different intelligent forecasting structures including neural network (NN) structure with BP tuning algorithm (NN-BP), FNN structure with BP tuning algorithm (FNN-BP), FNN structure with BP tuning algorithm and varied learning rates (FNN-BP-V), FNN structure with PSO tuning algorithm (FNN-PSO) and PSO structure are given by numerical simulations of a real case in Taiwan campus.
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基于模糊神经网络和粒子群优化的智能长期负荷预测设计
近年来,由可再生能源组成的智能微电网系统成为人们关注的研究课题之一。长期负荷预测(LTLF)的成功设计使智能微电网系统能够通过测量电力供应来操纵优化的加载和卸载控制,以实现最佳的经济和电力效率。本文基于模糊神经网络(FNN)和粒子群优化(PSO)的基本框架,构建了基于历史负荷变化率的相似时间方法的智能预测结构。在网络参数的调节方面,采用了传统的BP和PSO调谐算法,并根据离散时间李雅普诺夫稳定性理论设计了不同的学习率。通过台湾校园的实际案例,对神经网络(NN)结构与BP调谐算法(NN-BP)、FNN结构与BP调谐算法(FNN-BP)、FNN结构与BP调谐算法和变学习率(FNN-BP- v)、FNN结构与PSO调谐算法(FNN-PSO)和PSO结构等不同智能预测结构的性能进行了比较。
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