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Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model

机译:基于ANN的预测模型中WEKA的J48算法在喜马拉雅西部印度喜马al尔邦的太阳能潜力评估

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Solar potential of western Himalayan Indian state of Himachal Pradesh is assessed using Artificial Neural Network (ANN) based global solar radiation (GSR) prediction model. J48 algorithm in Waikato Environment for Knowledge Analysis (WEKA)is used for the selection of input parameters for ANN model for predicting GSR. Most relevant input parameters are found to be temperature, altitude and sunshine hours whereas latitude, longitude, clearness index and extraterrestrial radiation are found to be least influencing variables. The usefulness of J48 algorithm in variable selection is checked by developing five ANN models: ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5. The maximum mean absolute percentage error (MAPE) for ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5 are found to be 16.91%, 16.89%, 16.38%, 6.89% and 9.04% respectively. ANN-5 model is used to develop the solar maps of Himachal Pradesh. The estimated GSR varies from 3.59 to 5.38 kWh/m(2)/day indicating good solar potential for solar energy applications. A correlation is developed between NASA satellite data and ground measured GSR data to find values close to ground measured GSR for different locations. The correlation coefficient is found to be 0.97. Models developed can be used to assess solar potential of any location worldwide. (C) 2014 Elsevier Ltd. All rights reserved.
机译:使用基于人工神经网络(ANN)的全球太阳辐射(GSR)预测模型评估了喜马拉雅印度西部喜马al尔邦的太阳势。怀卡托知识分析环境(WEKA)中的J48算法用于选择用于预测GSR的ANN模型的输入参数。发现最相关的输入参数是温度,高度和日照时间,而纬度,经度,净度指数和地外辐射的影响参数最小。通过开发五个ANN模型(ANN-1,ANN-2,ANN-3,ANN-4和ANN-5)来检验J48算法在变量选择中的有用性。发现ANN-1,ANN-2,ANN-3,ANN-4和ANN-5的最大平均绝对百分比误差(MAPE)分别为16.91%,16.89%,16.38%,6.89%和9.04%。 ANN-5模型用于绘制喜马al尔邦的太阳图。估计的GSR从3.59到5.38 kWh / m(2)/天不等,表明太阳能应用具有良好的太阳能潜力。在NASA卫星数据与地面测量的GSR数据之间建立了相关性,以找到在不同位置接近地面测量的GSR的值。发现相关系数为0.97。开发的模型可用于评估全球任何位置的太阳能潜力。 (C)2014 Elsevier Ltd.保留所有权利。

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