Li, J., Zhang, B., Mao, C., Xie, G.L., Li, Y. and Lu, J. (2010) Wind Speed Prediction Based on the Elman Recursion Neural Networks. The 2010 International Conference on Modelling, Identification and Control (ICMIC), Okayama, 17-19 July 2010, 728-732.

has been cited by the following article:

• TITLE: ELMAN Neural Network with Modified Grey Wolf Optimizer for Enhanced Wind Speed Forecasting

  AUTHORS: M. Madhiarasan, S. N. Deepa

  KEYWORDS: ELMAN Neural Network, Modified Grey Wolf Optimizer, Hidden Layer Neuron Units, Forecasting, Wind Speed

  JOURNAL NAME: Circuits and Systems, Vol.7 No.10, August 16, 2016

  ABSTRACT: The scope of this paper is to forecast wind speed. Wind speed, temperature, wind direction, relative humidity, precipitation of water content and air pressure are the main factors make the wind speed forecasting as a complex problem and neural network performance is mainly influenced by proper hidden layer neuron units. This paper proposes new criteria for appropriate hidden layer neuron unit’s determination and attempts a novel hybrid method in order to achieve enhanced wind speed forecasting. This paper proposes the following two main innovative contributions 1) both either over fitting or under fitting issues are avoided by means of the proposed new criteria based hidden layer neuron unit’s estimation. 2) ELMAN neural network is optimized through Modified Grey Wolf Optimizer (MGWO). The proposed hybrid method (ELMAN-MGWO) performance, effectiveness is confirmed by means of the comparison between Grey Wolf Optimizer (GWO), Adaptive Gbest-guided Gravitational Search Algorithm (GGSA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Cuckoo Search (CS), Particle Swarm Optimization (PSO), Evolution Strategy (ES), Genetic Algorithm (GA) algorithms, meanwhile proposed new criteria effectiveness and precise are verified comparison with other existing selection criteria. Three real-time wind data sets are utilized in order to analysis the performance of the proposed approach. Simulation results demonstrate that the proposed hybrid method (ELMAN-MGWO) achieve the mean square error AVG ± STD of 4.1379e-11 ± 1.0567e-15, 6.3073e-11 ± 3.5708e-15 and 7.5840e-11 ± 1.1613e-14 respectively for evaluation on three real-time data sets. Hence, the proposed hybrid method is superior, precise, enhance wind speed forecasting than that of other existing methods and robust.