In modern years, wind energy has a significant development in the world. However, one of the major issues of power generated from wind is its uncertainty and resultant power. To solve the above-said problem, few approaches have been presented. In recent times, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. The Back-propagation (BP) neural network is then provided with the data to establish the relationship between the inputs and the output. Measured wind speeds, temperature, pressure and wind speed predicted outputs with each 10-min resolution for 15 th January 2015(24 hours) an existing wind power station, located at VSB-TUO, Ostrava, are integrated to form three types of input neuron numbers. In this, paper presents a short -term power prediction for a wind power plant located at VSB-TUO, Ostrava using multilayer ANN approach. Simulation results are reported, showing that the estimated wind speed values (predicted by the proposed network) are in good agreement with the experimental measured values.

Abstract-This

Purpose This paper aims to present a detailed investigation on the parameter estimation of a photovoltaic (PV) module by using a simplified two-diode model. Design/methodology/approach The studied PV module in this paper resembles an ideal two-diode model, and to reduce the computational time, the proposed model has a photocurrent source and two ideal-diodes and neglects the series and shunt resistances. Hence, for calculating the unknown parameters, only four parameters are required from the datasheet. Moreover, the studied model is simple and uses an easy modeling approach which is free from complexities. Findings The performance of the PV module is analyzed under non-standard test conditions by considering partial shading at different shaded levels, and it is found that the model has less computational time and gives accurate results. Originality/value The usefulness of this PV model is demonstrated with the help of several illustrative figures, and the performance of the PV module is evaluated.