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Author: search.filters.author.Kumar, RajneeshSubject: search.filters.subject.Clustering

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    Comparison of SOM and conventional neural network data division for PV reliability power prediction
    (IEEE, 2017) Kumar, Rajneesh
    This paper studies the influence of neural network clustering in power prediction of soiled PV panels using artificial neural networks. Self-organizing maps were used to cluster and preprocess the data before training the neural network. 70% of data from each cluster is used for training and 15% each for testing and validation. The accuracy of prediction from the developed model was compared with a neural network model which uses random data division without data preprocessing. It was observed that preprocessing the data through clustering would enhance the accuracy of prediction as compared to model developed without data preprocessing. At lower irradiance levels (200-400W/m 2 ) the percentage error in prediction was 8% and at higher irradiance levels (800-1200W/m 2 ) the error decreased to 2%.
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    Power prediction of soiled PV module with neural networks using hybrid data clustering and division techniques
    (Elsevier, 2016-08) Kumar, Rajneesh
    The performance of a neural network model depends on the data used to develop the model and nature of data preprocessing used in its training algorithm. This paper proposes a hybrid clustering algorithm to preprocess the data that is used for neural network training as well as data division technique to predict the power output of a soiled solar panel. Deterministic characteristics of soil on the panel namely particle size composition, X-ray diffraction (XRD) analysis and Fourier transform infrared spectroscopy (FTIR) analysis are used to model the soil. Artificial soiling experiment is conducted with each soil sample on the panel at irradiance level in the range of 200–1200 W/m2 for a set of 18 tilt angles to collect data of short circuit current (Isc) and open circuit voltage (Voc) leading to power output. NNR (Neural Network random) with random data division, NNF (Neural network fuzzy) with Fuzzy C Means clustering before training, NNK (Neural network k-means) with K-means data clustering and NNH (Neural network hybrid) with hybrid data clustering and division techniques are developed using these data. The performance of these networks for a known and unknown soil samples is compared. For a known soil sample, NNH performed better (Maximum percentage error of 2%) as compared to NNR (−6.3%), NNF (−6.7%) and NNK (13%). In case of unknown soil sample NNH outperforms other models with a maximum error margin as low as (−10%) as compared to NNR (50%), NNF (−81%) and NNK (124%).