Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System

Due to various weather perturbation effects, the stochastic nature of real-life solar irradiance has been a major issue for solar photovoltaic (PV) system planning and performance evaluation. This paper aims to discover clearness index (CI) patterns and to construct centroids for the daily CI profiles. This will be useful in being able to provide a standardized methodology for PV system design and analysis. Four years of solar irradiance data collected from Johannesburg (26.21 S, 28.05 E), South Africa are used for the case study. The variation in CI could be significant in different seasons. In this paper, cluster analysis with Gaussian mixture models (GMM), K-Means with Euclidean distance (ED), K-Means with Manhattan distance, Fuzzy C-Means (FCM) with ED, and FCM with dynamic time warping (FCM DTW) are performed for the four seasons. A case study based on sizing a stand-alone solar PV and storage system with anaerobic digestion biogas power plants is used to examine the usefulness of the clustering results. It concludes that FCM DTW and GMM can determine the correct PV farm rated capacity with an acceptable energy storage capacity, with 36 and 46 rather than 1457 solar irradiance profiles, respectively.

[1]  Cordelia Schmid,et al.  High-dimensional data clustering , 2006, Comput. Stat. Data Anal..

[2]  Soteris A. Kalogirou,et al.  Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system , 2008 .

[3]  A. Bernardos,et al.  A comparison of one-minute probability density distributions of global horizontal solar irradiance conditioned to the optical air mass and hourly averages in different climate zones , 2015 .

[4]  L. Umanand,et al.  Estimation of global radiation using clearness index model for sizing photovoltaic system , 2005 .

[5]  Wei Gong,et al.  An improved method for estimating the Ångström turbidity coefficient β in Central China during 1961–2010 , 2015 .

[6]  Christos S. Ioakimidis,et al.  Wind Power Forecasting in a Residential Location as Part of the Energy Box Management Decision Tool , 2014, IEEE Transactions on Industrial Informatics.

[7]  Changming Zhu,et al.  Multiple Matrix Learning Machine with Five Aspects of Pattern Information , 2015, Knowl. Based Syst..

[8]  A. Guessoum,et al.  Classification of daily solar irradiation by fractional analysis of 10-min-means of solar irradiance , 2005 .

[9]  Witold Pedrycz,et al.  Fuzzy clustering of time series data using dynamic time warping distance , 2015, Eng. Appl. Artif. Intell..

[10]  Kadir Bakirci,et al.  Models for the estimation of diffuse solar radiation for typical cities in Turkey , 2015 .

[11]  Yunming Ye,et al.  Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Wenyuan Li,et al.  Chronological Probability Model of Photovoltaic Generation , 2014, IEEE Transactions on Power Systems.

[13]  John F. Kolen,et al.  Reducing the time complexity of the fuzzy c-means algorithm , 2002, IEEE Trans. Fuzzy Syst..

[14]  O. Kisi,et al.  Solar radiation prediction using different techniques: model evaluation and comparison , 2016 .

[15]  Pinar Karagoz,et al.  A Novel Wind Power Forecast Model: Statistical Hybrid Wind Power Forecast Technique (SHWIP) , 2015, IEEE Transactions on Industrial Informatics.

[16]  Chao-Ming Huang,et al.  A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output , 2014, IEEE Transactions on Sustainable Energy.

[17]  R. Belmans,et al.  Voltage fluctuations on distribution level introduced by photovoltaic systems , 2006, IEEE Transactions on Energy Conversion.

[18]  M. Muselli,et al.  Classification of typical meteorological days from global irradiation records and comparison between two Mediterranean coastal sites in Corsica Island , 2000 .

[19]  Tawanda Hove,et al.  Estimates of the Linke turbidity factor over Zimbabwe using ground-measured clear-sky global solar radiation and sunshine records based on a modified ESRA clear-sky model approach , 2013 .

[20]  Alexander Mendiburu,et al.  Similarity Measure Selection for Clustering Time Series Databases , 2016, IEEE Transactions on Knowledge and Data Engineering.

[21]  Chigueru Tiba,et al.  Cumulative distribution curves of daily clearness index in a southern tropical climate , 2007 .

[22]  Charles Bouveyron,et al.  Model-based clustering of high-dimensional data: A review , 2014, Comput. Stat. Data Anal..

[23]  Jan Skoglund,et al.  Vector quantization based on Gaussian mixture models , 2000, IEEE Trans. Speech Audio Process..

[24]  Miin-Shen Yang,et al.  A robust EM clustering algorithm for Gaussian mixture models , 2012, Pattern Recognit..

[25]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[26]  F. Kasten.  The linke turbidity factor based on improved values of the integral Rayleigh optical thickness , 1996 .

[27]  K F Katiraei,et al.  Solar PV Integration Challenges , 2011, IEEE Power and Energy Magazine.

[28]  Lunche Wang,et al.  Comparison of different UV models for cloud effect study , 2015 .

[29]  T. E. Boukelia,et al.  General models for estimation of the monthly mean daily diffuse solar radiation (Case study: Algeria) , 2014 .

[30]  Kasra Mohammadi,et al.  Prediction of horizontal diffuse solar radiation using clearness index based empirical models; A case study , 2016 .

[31]  Temitope Raphael Ayodele,et al.  Prediction of monthly average global solar radiation based on statistical distribution of clearness index , 2015 .

[32]  Christian Hennig,et al.  Recovering the number of clusters in data sets with noise features using feature rescaling factors , 2015, Inf. Sci..

[33]  J. Appelbaum,et al.  Evaluation of solar radiation properties by statistical tools and wavelet analysis , 2013 .

[34]  Omid Nematollahi,et al.  Clearness index predicting using an integrated artificial neural network (ANN) approach , 2016 .

[35]  R. Belmans,et al.  Fluctuations in instantaneous clearness index: Analysis and statistics , 2007 .

[36]  S. Harrouni,et al.  Preliminary results of the fractal classification of daily solar irradiances , 2003 .

[37]  Chun Sing Lai,et al.  Sizing of Stand-Alone Solar PV and Storage System With Anaerobic Digestion Biogas Power Plants , 2017, IEEE Transactions on Industrial Electronics.

[38]  T. Soubdhan,et al.  Classification of daily solar radiation distributions using a mixture of Dirichlet distributions , 2009 .

[39]  Antonio Sanfilippo,et al.  An adaptive multi-modeling approach to solar nowcasting , 2016 .

[40]  Chunxiang Yang,et al.  An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data , 2015, IEEE Transactions on Sustainable Energy.

[41]  Manoj Kumar Tiwari,et al.  Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch , 2008, IEEE Transactions on Evolutionary Computation.

[42]  Yakup S. Ozkazanç,et al.  Wind Pattern Recognition and Reference Wind Mast Data Correlations With NWP for Improved Wind-Electric Power Forecasts , 2016, IEEE Transactions on Industrial Informatics.

[43]  Catherine Rosenberg,et al.  Solar Power Shaping: An Analytical Approach , 2015, IEEE Transactions on Sustainable Energy.

[44]  Pierre Gançarski,et al.  A global averaging method for dynamic time warping, with applications to clustering , 2011, Pattern Recognit..