Evaluation and Mapping of Wind Energy Potential over Southern Part of India using ANN and GIS Approach

Khalid Anwar, S. Deshmukh
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引用次数: 1

Abstract

Prediction and assessment of wind speed are necessary prerequisites in the sitting and sizing of wind power applications. In this study, an artificial neural network (ANN) model was developed for prediction of wind energy potential in Andhra Pradesh (AP) and Telangana state (TS), India. ANN models are ‘black-box’ modelling technique, with capability to perform nonlinear mapping of a multidimensional input space onto another multidimensional output space without the knowledge of the dynamics of the relationship between the input and output spaces. The geographical parameters (latitude, longitude and altitude) and the month of the year were used as input data, while the monthly mean wind speed was used as the output of the network. Geographical and meteorological data of 30 cities in AP and TS of 20 years (1995–2015) by the India meteorological department, Pune (IMD-Pune) database were used for the training and testing the network. The testing data were not used in the training of the network in order to give an indication of the performance of the system at unknown locations. Statistical error analysis in terms of mean absolute percentage error (MAPE) was conducted for testing data to evaluate the performance of ANN model.
利用人工神经网络和地理信息系统方法对印度南部风能潜力进行评价和制图
风速的预测和评估是风力发电项目选址和规模确定的必要前提。在这项研究中,开发了一个人工神经网络(ANN)模型来预测印度安得拉邦(AP)和特伦甘纳邦(TS)的风能潜力。人工神经网络模型是一种“黑盒”建模技术,能够在不了解输入和输出空间之间动态关系的情况下,将一个多维输入空间非线性映射到另一个多维输出空间。地理参数(纬度、经度和海拔)和年份作为输入数据,月平均风速作为网络输出。使用印度气象部门浦那(IMD-Pune)数据库提供的AP和TS 20年(1995-2015)30个城市的地理和气象数据进行网络的训练和测试。测试数据不用于网络的训练,以便在未知位置给出系统性能的指示。对测试数据进行平均绝对百分比误差(MAPE)统计误差分析,以评价人工神经网络模型的性能。
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