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Human needs increasing over years, energy is needed to fulfill the daily life. In the other hands, source of energy such as coal is decrease and become less and less. There is a renewable energy that can made from wind source. Wind energy can be obtained by using some tools such as wind turbine. Wind turbine is a device that used to convert wind energy into electricity. Wind turbine has developed to many variation. One of variation that added to the wind turbine especially on their blades is splitted winglet. This additional variation is an adaptation from recent aircraft. The advantages of using this typical of winglets on aircraft is increasing the lift coefficient and decreasing drag coefficient. The function of splitted winglet on wind turbine blades is to minimize the backflow on the tip of blades. This research expectation is turbine will get better performance. Comparing the turbine with the blade without winglets, with winglets and with splitted winglets. Turbine with the blades without winglets gives best performance among all the turbine variations. The air flow hit the turbine at 6 m/s, the performance of turbine without winglets give Cp about 5,2E-1. With the same velocity of airflow, the turbine with winglets and splitted winglets give Cp about 4,7E-1 and 2,1E-1. IntroductionNowadays, most of power plant that produce electricity from coal which is its source is decreasing over the years. Following by human needs of electricity to fulfill their life is increasing every time. One of solution that can solve this problem is by using renewable energy such as wind energy. Wind energy can be converted to make an electricity by using wind turbine. There are so many kind of wind turbine. From the axis of rotation, there are horizontal axis wind turbine and vertical axis wind turbine. Horizontal axis wind turbine give some advantages such as for using of area, fees, and noise. The turbine performance affected by external and internal factor. The external factor come from the ambiance condition including weather. The internal factor that affected in turbine performance come from turbine component such as blades, tail, generator, and its transmissions. Blades make the rotation of turbine to make an electricity. This device can make the turbine's performance become better by adding some variation. One of the variation that can added into the blades is splitted winglet.Splitted winglet is the innovation from blended winglet that added by another winglet in the against direction. This additional variation is adapted from the recent aircraft. The purpose of adding this variation is to enhance the performance so that there are minimalize the back flow on the blade. From the research [1] the wings or aircraft with splitted winglet has better performance than wings of aircraft using blended winglet or single winglets.

Solar power is a renewable energy interest many researchers around the world to be explored for human life beneficial especially for electric power generation. Photovoltaic (PV) is one of technology developed massively to exploit the solar power for this purpose. However, its performance is very sensitive to environmental condition such as solar irradiance, weather, and climatic behavior. Thus, the hybrid power generation systems are developed to solve this output uncertainty problem. To support this such hybrid system, this paper proposes an ensemble neural network based forecaster of the power output of PV systems which will lead an efficient power management. The object of this research is the PV systems equipped with two axes automated solar tracking with peak power 10Wp. The proposed ensemble forecaster model employs four multi-layer perceptron neural networks with two hidden layers as base forecasters while the input number of historical data is varied in order to exploit the forecaster diversity. The final prediction is calculated both by conventional averaging and simple weighting optimized by the least square fitting technique. According to the research results, the both proposed approaches provide low error rate. Moreover, in term of comparison, the ensemble model with averaging combining technique gives the highest accuracy comparing to the other ensemble and conventional neural network structures.

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