Feature Selection Optimization Solar Insolation Prediction Using Artificial Neural Network: Perspective Bangladesh
Journal Title: American journal of Engineering Research - Year 2016, Vol 5, Issue 8
Abstract
This paper presents a new climatological feature selection model using artificial neural network by utilizing the real world data for Bangladesh, called as FSOSIP. In this analysis back propagation algorithm is applied. To facilitate the search strategy FSOSIP model uses 12 different combinations of 10 climatological features and a series of experiments were done to select the best subset of relevant features. The monthly averaged data has been collected from Bangladesh Metrological Department for seven stations as Dhaka, Barisal, Chittagong, Khulna, Rajshahi, Rangpur and Sylhet. The 1176 data between 2000-2013 are used to train the neural networks while the 365 data from 2014 are used to test the neural network. Experimental results show that the proposed FSOSIP model can select only two salient features easily with increasing the prediction accuracy which are relative humidity and maximum temperature. Furthermore, to prove the robustness of the model seven different models for seven stations of BMD are used and the results are then being averaged. Minimum MSE (0.000173%) and MAPE (0.0868%) shows the higher efficiency to predict the solar insolation. In addition, the proposed model exhibit better performance with feature selection strategy than any other model used up to now in Bangladesh.
Authors and Affiliations
Md. Shahid Iqbal1 ,, Md. Ataullah Mishkat2 ,, S. M. Rashedul Islam3 ,, Md. Shariful Islam4
Keywords
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