Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid
作者: Badar IslamZuhairi BaharudinPerumal Nallagownden
作者单位: 1Universiti Teknologi PETRONAS
刊名: Neural Computing and Applications, 2017, Vol.28 (1), pp.877-891
来源数据库: Springer Nature Journal
DOI: 10.1007/s00521-016-2408-3
关键词: Artificial neural networkDemand responseSmart gridReal-coded genetic algorithmElectrical energy demand predictionChaotic mappingSimulated annealing
原始语种摘要: In this paper, a modified backpropagation neural network is combined with a chaos-search genetic algorithm and simulated annealing algorithm for very short term electrical energy demand prediction in deregulated power industry. Multiple modifications are carried out on the conventional backpropagation algorithm such as improvements in the momentum factor and adaptive learning rate. In the hybrid scheme, the initial parameters of the modified neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. The solution set provided by the optimized genetic algorithm is further improved by using the strong local search ability of simulated annealing algorithm. The real data of New South Wales,...
全文获取路径: Springer Nature  (合作)
影响因子:1.168 (2012)

  • demand 需用电力
  • improved 改进
  • neural 神经系统的
  • prediction 预报
  • heuristics 试探
  • algorithm 算法
  • electrical 电的
  • genetic 遗传的
  • network 网络
  • ability 能力