Artificial Neural Networks to Predict the Power Output of a PV Panel
作者: Valerio Lo BranoGiuseppina CiullaMariavittoria Di FalcoDavid Worrall
作者单位: 1DEIM Università degli studi di Palermo, Viale Delle Scienze, Edificio 9, 90128 Palermo, Italy
刊名: International Journal of Photoenergy, 2014, Vol.2014
来源数据库: Hindawi Journal
DOI: 10.1155/2014/193083
原始语种摘要: The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate...
全文获取路径: Hindawi 

  • forecasting 预报
  • monitoring 监视
  • suitability 适应
  • methodology 方法学
  • system 
  • electricity 电气
  • output 输出
  • problem 题目
  • approach 
  • trained 受过训练的