Wind speed and power prediction of prominent wind power potential states in India using GRNN

Savita, M. A. Ansari, N. Pal, H. Malik
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引用次数: 10

Abstract

This paper introduces Generalized Regression Neural Network (GRNN) for long term wind speed prediction of major wind power potential states in India. The performance of proposed GRNN model is evaluated using the publicly available online dataset of National Aeronautics and Space Administration (NASA). Data samples of 26 cities are used for training the generalized regression neural network and remaining 5 cities data samples are used for testing purpose. Air temperature, earth temperature, relative humidity, daily solar radiation, elevation, latitude, heating degree days, cooling degree days, frost days, longitude and atmospheric pressure are used as input variables. Mean square error between measured and forecasted wind speed using training data samples and testing data samples are found to be 0.000042279 and 0.1543. Here it is important to impart that the proposed GRNN model is trained and tested with data samples of different geographical locations in order to make it feasible for wind speed prediction of any other location. Wind power of prominent wind power potential states in India are predicted by a variable pitch and speed control wind turbine G80-2MW.
利用GRNN对印度主要风电潜力邦的风速和功率进行预测
本文将广义回归神经网络(GRNN)应用于印度主要风电潜力邦的长期风速预测。利用美国国家航空航天局(NASA)公开的在线数据集对所提出的GRNN模型的性能进行了评估。26个城市的数据样本用于训练广义回归神经网络,其余5个城市的数据样本用于测试。输入变量为气温、地球温度、相对湿度、日太阳辐射、高程、纬度、加热度天数、冷却度天数、霜冻天数、经度和大气压。使用训练数据样本和测试数据样本测量风速和预测风速的均方误差分别为0.000042279和0.1543。在这里,重要的是要指出,所提出的GRNN模型是用不同地理位置的数据样本进行训练和测试的,以便使其适用于任何其他位置的风速预测。印度风力发电潜力突出的州的风力发电是由一个可变螺距和速度控制的G80-2MW风力涡轮机预测的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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