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Prediction of solar power involves the knowledge of the sun, atmosphere and other parameters, and the scattering processes and the specifications of a solar energy plant that employs the sun's energy to generate solar power. This prediction result is essential for an efficient use of the solar power plant, the management of the electricity grid, and solar energy trading. However, because of nonlinear and nonstationary behavior of solar power time series, an efficient forecasting model is needed to predict it. Accordingly, in this paper, we propose a new forecast approach based on combination of a neural network with a metaheuristic algorithm as the hybrid forecasting engine. The metaheuristic algorithm optimizes the free parameters of the neural network. This approach also includes a 2‐stage feature selection filter based on the information‐theoretic criteria of mutual information and interaction gain, which filters out the ineffective input features. To demonstrate the effectiveness of the proposed forecast approach, it is implemented on a real‐world engineering test case. Obtained results illustrate the superiority of the proposed approach in comparison with other prediction methods.

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In this article, a new metaheuristic optimization algorithm is introduced. This algorithm is based on the ability of shark, as a superior hunter in the nature, for finding prey, which is taken from the smell sense of shark and its movement to the odor source. Various behaviors of shark within the search environment, that is, sea water, are mathematically modeled within the proposed optimization approach. The effectiveness of the suggested approach is compared with many other heuristic optimization methods based on standard benchmark functions. Also, to illustrate the efficiency of the proposed optimization method for solving real-world engineering problems, it is applied for the solution of load frequency control problem in electrical power systems. The obtained results confirm the validity of the proposed metaheuristic optimization algorithm.

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Summary In this paper a new model of radial basis function (RBF) neural network based on a novel stochastic search algorithm is presented for short‐term load forecast (STLF). STLF is an important operation function in both regulated and deregulated power systems. Accurate STLF is effective for area generation control and resource dispatch as well as electricity market clearing. The proposed STLF method optimizes the structure of the RBF‐based forecasting engine. Random selection of the number of hidden neurons may cause overfitting or underfitting problem of the network. For this purpose, a new stochastic search algorithm is presented to find the optimum number of neurons for the hidden layer. To demonstrate the effectiveness of the proposed STLF approach, it is tested on three real‐world engineering case studies, and the obtained results are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach. Copyright © 2015 John Wiley & Sons, Ltd.

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