In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.
Power generation using wind has been extensively utilised, with substantial capacity add-on worldwide, during recent decades. The wind power energy sector is growing, and has turned into a great source of renewable power production. In the past decades of the 21st century, the capacity of installed wind energy has almost doubled every three years. This review paper presents the crucial facets and advancement strategies that were approved and adopted by the Government of India for intensifying the country’s own power safety, by the appropriate use of existing power sources. From India’s viewpoint, wind energy is not only utilized for power production but also to provide power in a more economical way. The particulars of India’s total energy production, contributions of numerous renewable sources and their demand are also encompassed in this paper. After an exhaustive review of the literature, detailed facts have been identified about the present position of wind energy, with an emphasis on government achievements, targets, initiatives, and various strategic advances in the wind power sector. Wind power potential is discussed, which can assist renewable power companies to select efficient and productive locations. All analyses carried out in this paper will be incredibly valuable to future renewable energy investors and researchers. The current scenario of wind power production in India is also paralleled with that of other globally prominent countries.
Short-term wind power forecasting is crucial for power system stability, dispatching, and cost control. Wind energy has the potential to be a viable source of renewable energy. Wind power generation forecasting is vital for resolving the supply and demand challenges of the smart grid. Moreover, one of the most problematic aspects of wind power is its high fluctuation and intermittent nature, which makes forecasting difficult. The goal of this research is to create machine learning models that can properly estimate wind power production. Significantly, the major contributions of this work are highlighted in the following significant elements. First, a data analysis framework for visualizing the gathered dataset from the SCADA system is presented. Second, for forecasting wind power time-series dataset values, we examine the predicting performance of various machine learning models using various statistical indices. The experimental findings demonstrate that with a minor reconstruction error, the proposed forecast approaches can minimize the complexity of the forecasting. Furthermore, in terms of forecast accuracy, a gradient boosting regression model outperforms other benchmark models. According to the analysis, our methodology might be applied in real-world circumstances to assist the management group in regulating the power provided by wind turbines.
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