Short term wind speed predicting is essential in using wind energy as an alternative source of electrical power generation, thus the improvement of wind speed prediction accuracy becomes an important issue. Although many prediction models have been developed during the last few years, they suffer a poor performance becausetheir dependency on performing only the local search without the capability in performing the global search in the whole search space.To overcome this problem, we propose a new passive congregation term to the standard hybrid Genetic Algorithm / Particle Swarm Optimization (GA/ PSO) model in training Neural Network (NN) wind speed predictor. This term is based on the mutual cooperation between different particles in determining new positions rather than their selfish thinking. Experiment study shows significantly the influence of the passive congregation term in improving the performance accuracy compared to the standard model. General TermsWind Speed Prediction, Key wordsParticle Swarm Optimization, Genetic Algorithm, Neural Networks, Passive Congregation. INTRODUCTIONWind energy is one of the lowest costs of electricity production among renewable energy sources, but is visible only as long as weather conditions allow, so to maintain economical dispatch of wind generation electricity, it is important to make short term predictions of future wind speed which directly affects generation capacity [1]. Without this ability, a wind farm operator is prone to allocate more generation units or supplemental energy reserves than necessary in order to ensure budgeted electricity outputs are met, with an end result of increased operating costs [2]. Thus, the further prediction accuracy improvement of the wind speed predication becomes a fundamental issue in the wind industry [2-3].In short term wind speed prediction, Feedforward Neural Networks (FNN) trained using tabu search [4] and Recurrent Neural Networks (RNN) [5] have been used.Particle Swarm Optimization (PSO) which is a population based stochastic algorithm was used in training both FNN and RNN wind speed predictors to enhance their prediction accuracy [2]. Also, Genetic Algorithm (GA) operators was introduced to increase the training accuracy of PSO learning algorithm, to form GA/ PSO hybrid model to enable searching new regions in the search space [6][7].However, in GA/ PSO model, the PSO algorithm is restricted in doing the local search around its best position and the global best position without any cooperation with other particles in the swarm while the GA operators are responsible in doing the global search by making particles fly to new regions in the search space leading to poor model performance in terms of accuracy, convergence speed and robustness [8].To overcome this problem and improve the model performance, each particle in the swarm should be capable of performing the global search and not to be only restricted in local search [9]. Thus, we introduce our new proposed hybrid (GA/PSO) model with passive congregation inspired ...

The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems. This is done by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoid function making their outputs not restricted to the sigmoid function output. Also, we introduce a dynamic back propagation learning algorithm to train the new proposed network parameters. The simulation results showed that the (SDRNN) is more efficient and accurate than the DRNN in both the identification and adaptive control of nonlinear dynamical systems.

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The electricity production of wind farms is fluctuating because its dependency on the wind speed, so, improving of technical and economic integration of wind energy into the electricity supply system requires improving wind speed prediction accuracy. Thus, we propose a new hybrid model by adding the passive aggregation represented by an appropriate physical force to increase wind speed prediction accuracy. Experiment study shows significantly the influence of the passive aggregation in improving the prediction accuracy.  Index Terms-hybrid model, Passive aggregation, Physical force, Wind speed prediction. I. Tarek Aboueldahab was born in April 1971 and obtained the bachelor degree in electronics and communications engineering from faculty of engineering-Cairo University in 1993 and a master degree in electronic and communications engineering, nonlinear control sector from the same University in 1998. He is working in Cairo Metro Company, Egyptian Ministry of Transport since 1995 and he is now the manager of research and development. He is also an Academic Community Member in the International Congress for global Science and Technology. His fields of interest include non-linear control, artificial intelligence application, particle swarm optimization, genetic algorithms and neural networks.