维多利亚州电力需求的神经模糊建模方法
A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria
摘要：
由于智能系统的需求日益增长，模糊神经系统引起了科学和工程领域研究人员的广泛兴趣。本文评估了使用两种流行的软计算技术和传统的统计方法的基础上Box-詹金斯自回归综合移动平均（ARIMA）模型预测电力需求在澳大利亚维多利亚州。考虑软计算方法是一个进化的模糊神经网络（EFuNN）和人工神经网络（ANN）训练使用缩放共轭梯度算法（CGA）和反向传播（BP）算法。预测精度与维多利亚电力交易所（VPX）和实际能源需求的预测进行了比较。为了进行评估，我们考虑了连续10个月的负荷需求模式，每30分钟进行一次，以训练不同的预测模型。测试结果表明，神经模糊系统的性能优于神经网络，ARIMA模型和VPX预测。
英文摘要：
Neuro-fuzzy systems have attracted growing interest of researchers in various scientific and engineering areas due to the increasing need of intelligent systems. This paper evaluates the use of two popular soft computing techniques and conventional statistical approach based on Box--Jenkins autoregressive integrated moving average (ARIMA) model to predict electricity demand in the State of Victoria, Australia. The soft computing methods considered are an evolving fuzzy neural network (EFuNN) and an artificial neural network (ANN) trained using scaled conjugate gradient algorithm (CGA) and backpropagation (BP) algorithm. The forecast accuracy is compared with the forecasts used by Victorian Power Exchange (VPX) and the actual energy demand. To evaluate, we considered load demand patterns for 10 consecutive months taken every 30 min for training the different prediction models. Test results show that the neuro-fuzzy system performed better than neural networks, ARIMA model and the VPX forecasts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
