ESTIMATING AMBIENT TEMPERATURE FOR MALAYSIA USING GENERALIZED REGRESSION NEURAL NETWORK

Tamer Khatib; Azah Mohamed; K. Sopian; M. Mahmoud

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ESTIMATING AMBIENT TEMPERATURE FOR MALAYSIA USING GENERALIZED REGRESSION NEURAL NETWORK

机译：用广义回归神经网络估计马来西亚的环境温度

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This paper presents a new method for predicting hourly ambient temperature series for Malaysia using generalized regression neural network (GRNN). MATLAB was used to develop the GRNN using the weather records for Malaysia. The developed model has five inputs and one output. The inputs of the proposed model are hour, day, month, sunshine ratio, and relative humidity, meanwhile ambient temperature is the output. To evaluate the accuracy of the GRNN, three statistical parameters, namely, the mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE) are considered. The GRNN results give an accurate prediction of ambient temperatures for the selected testing months with average values of MAPE, MBE, and RMSE of 2.65%, 4.05%, and 0.347%, respectively. The advantage of the proposed method is that it is able to predict ambient temperature at sites where there is no ambient temperature-measuring instrument installed.

机译：本文提出了一种使用广义回归神经网络（GRNN）预测马来西亚每小时气温序列的新方法。使用MATLAB来利用马来西亚的天气记录来开发GRNN。开发的模型具有五个输入和一个输出。该模型的输入是小时，日，月，日照率和相对湿度，而环境温度是输出。为了评估GRNN的准确性，考虑了三个统计参数，即平均绝对百分比误差（MAPE），平均偏差误差（MBE）和均方根误差（RMSE）。 GRNN结果可准确预测选定测试月份的环境温度，MAPE，MBE和RMSE的平均值分别为2.65％，4.05％和0.347％。所提出的方法的优点在于它能够预测没有安装环境温度测量仪器的地点的环境温度。

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《International journal of green energy》 |2012年第4期|p.195-201|共7页
作者
Tamer Khatib; Azah Mohamed; K. Sopian; M. Mahmoud;
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作者单位

Department of Electrical, Electronic & System Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia;

Department of Electrical, Electronic & System Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia;

Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;

Department of Electrical Engineering, Engineering Faculty, An-Najah National University, Nablus, Palestine;

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原文格式 PDF
正文语种 eng
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ambient temperature prediction; ANN; malaysia;

机译：环境温度预测;人工神经网络马来西亚;

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ESTIMATING AMBIENT TEMPERATURE FOR MALAYSIA USING GENERALIZED REGRESSION NEURAL NETWORK

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