Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.

Images are an important medium to represent meaningful information. It may be difficult for computer vision techniques and humans to extract valuable information from images with low illumination. Currently, the enhancement of low-quality images is a challenging task in the domain of image processing and computer graphics. Although there are many algorithms for image enhancement, the existing techniques often produce defective results with respect to the portions of the image with intense or normal illumination, and such techniques also inevitably degrade certain visual artifacts of the image. The model use for image enhancement must perform the following tasks: preserving details, improving contrast, color correction, and noise suppression. In this paper, we have proposed a framework based on a camera response and weighted least squares strategies. First, the image exposure is adjusted using brightness transformation to obtain the correct model for the camera response, and an illumination estimation approach is used to extract a ratio map. Then, the proposed model adjusts every pixel according to the calculated exposure map and Retinex theory. Additionally, a dehazing algorithm is used to remove haze and improve the contrast of the image. The color constancy parameters set the true color for images of low to average quality. Finally, a details enhancement approach preserves the naturalness and extracts more details to enhance the visual quality of the image. The experimental evidence and a comparison with several, recent state-of-the-art algorithms demonstrated that our designed framework is effective and can efficiently enhance low-light images.

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In this paper comparative analysis of maximum power point tracking techniques has been conducted to achieve highest magnitude of power from photovoltaic array. The algorithms proposed in this paper for extracting peak output from photovoltaic array are Perturb and Observe, Incremental Conductance, and Fuzzy Logic Control. There are some limitations with conventional converters i.e. Buck-Boost converter. When the operating voltage exceeds normal voltage as the voltage becomes high, the conventional converters fail to carry high voltage and current. Apart from this the ripple contents also increase abnormally due to the large impedance in the conventional converter. Similarly these converters cannot track maximum power point faster and effectively. In that case Single Ended Primary Inductor Converter (SEPIC) is the best choice instead of the conventional buck-boost converter, which is employed with the aim of extracting maximum output from the photovoltaic array. The aim of this study is to compare three MPPT techniques under varying environmental conditions with respect to maximum power extraction and speed of tracking time. SEPIC is used instead of conventional buck-boost converter in order to achieve maximum efficiency and less ripples. Also it can track maximum power point (MPP) faster than Buck-Boost Converter. Comparative analysis of three most extensively used MPPT techniques have been conducted in Simulink/Matlab.