As a green energy source, the use of wind has been rapidly growing in recent years. Whereas wind has complex and stochastic nature hence precise wind power predictions are essential for economic operation of the wind energy systems. For utilities, the rapid variations in wind power can generate serious problem of reliability reduction. The forecasting of wind power changes allows a utility to plan the connection and disconnection of wind power generation based on forecasting wind power generation and predicted load. In this paper, an environment friendly wind power prediction technique of variable-speed wind power system is proposed. The technique is employed from the prediction algorithm to create a prediction model to get accurate power. It is authenticated on the producer power curve of the variable-speed wind system. Additionally, the technique is used in average monthly wind power prediction and the outcomes show a huge improvement in prediction accuracy using the proposed method. Further, the likely value of rated wind speed for installed wind power system in Vishakhapatnam, Bhopal, Ahmedabad, Thiruvananthapuram, Bangalore, India, are also discussed. The empirical outcomes are compared with different wind forecast models and based on the root mean square error (RMSE), the proposed model gives the perfection in prediction accuracy compared to Gaussian Processes and Numerical Weather Prediction, Wind power prediction without adjustment, Wind power prediction with adjustment, support vector machine methods. Further, the developed model is used to evaluate the annual reliability indices by convolving the predicted generation with predicted load in the selected station.

作为一种绿色能源，风能的利用近年来迅速增长。鉴于风具有复杂性和随机性，因此精确的风功率预测对于风能系统的经济运行至关重要。对于公用事业而言，风力发电的快速变化会产生可靠性降低的严重问题。风电功率变化预测允许电力公司根据风力发电预测和预测负荷来规划风力发电的连接和断开。本文提出了一种环境友好的变速风电系统风电功率预测技术。从预测算法中采用该技术来创建预测模型以获得准确的功率。它在变速风系统的生产者功率曲线上得到验证。此外，该技术被用于月平均风功率预测，结果表明使用所提出的方法预测精度有了巨大的提高。此外，还讨论了印度班加罗尔 Vishakhapatnam、Bhopal、Ahmedab​​ad、Thiruvananthapuram 的已安装风力发电系统的额定风速可能值。将经验结果与不同的风力预报模型进行比较，并基于均方根误差 (RMSE)，与高斯过程和数值天气预报、无调整的风力预测、有调整，支持向量机方法。此外，开发的模型用于通过将预测的发电量与所选站点的预测负荷进行卷积来评估年度可靠性指标。