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.