Source :
8th International Conference on Modelling, Identification and Control (ICMIC-2016)
Descriptors :
Phase space reconstruction , DBSCAN , k-means , fuzzy c-means , solar radiation , clustering
Abstract :
the study of the dynamic behaviour of the solar
radiation is a very important task for PV system efficiency.
Hence, we propose in this paper, a time series data mining
method to detect the underlying dynamic presents in hourly solar
radiation time series. Density-Based Spatial Clustering of
Applications with Noise (DBSCAN ) is used to cluster the solar
radiation time series and detect noisy data. Moreover, the
proposed method is compared with two unsupervised clustering
techniques, k-means and Fuzzy c-means, for the analysis of the
measured hourly solar radiation time series. All the algorithms
are focused on extracting useful information from the data with
the aim of model the time series behaviour and find patterns to
be used in PV system applications. This electronic document is a
“live” template and already defines the components of your
paper [title, text, heads, etc.] in its style sheet
Title of Article :
Density-Based Spatial Clustering of Application with Noise Algorithm for the Classification of Solar Radiation Time Series
Author/Authors :
BENMOUIZA Khalil , CHEKNANE Ali
Author/Authors - جزئيات :
