Clustering the solar resource for grid management in island mode

Zagouras, Athanassios; Pedro, Hugo T.C.; Coimbra, Carlos F.M.

doi:10.1016/j.solener.2014.10.002

Cited by 23 publications

(13 citation statements)

References 25 publications

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“…Munshi and Mohamed (2016) apply a set of clustering methods from different clustering categories to determine the optimum number of clusters for photovoltaic power patterns data. Zagouras et al (2014) investigated the development of maps created by the combination of two well-known clustering techniques; i.e, the affinity propagation and the kmeans. This methodology makes it possible to select candidate locations for solar power plants, to determine regions of coherent solar quality attributes and to improve solar forecasting for PV plants.…”

Section: An Overview Of Literaturementioning

confidence: 99%

Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Frimane

Soubdhan²,

Bright

et al. 2019

Solar Energy

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High resolution synthetic irradiance is of interest for theoretical studies such as grid integration of solar PV and battery storage analysis. Access to site-specific data is often limited to inadequate temporal resolutions for such application. A new model for producing synthetic solar global horizontal irradiance (GHI) time-series at up to 1-min resolution is presented as derived from >10-min input data. Briefly, it is a clustered-based method for daily clearness index distributions using Dirichlet process Gaussian mixture model (DPGMM). DPGMM is a nonparametric Bayesian (NPB) model indexed with an infinite-dimensional space of parameters. The key benefit of the NPB paradigm is the automatic adaptation to the correct complexity level and model size, suggesting a local adaptation of the model to all climatic conditions. A posterior inference using Markov chain Monte Carlo algorithm (namely Gibbs sampling) is applied. The model only requires a valid number of intraday data to construct daily distributions, then it can be applied worldwide. The synthetic GHI time series are validated against observed 1-min GHI data for four locations distributed throughout the world with different climatic conditions and significant geographic separation. Moreover, the presented method can generate data based on similar climatic conditions. A good fit between real and generated data is observed. We present an nRMSE ≤ 4% and nMBE < ±4% between generated and measured means at both daily and monthly scales for all sites. The agreement between the real and generated cumulative density distributions of six comparative variability metrics (defined in text) at four different sites is measured using the overlapping and the Kullback-Leibler coefficients, which are ≥ 75% and ≤ 10% respectively, in all cases. To ensure the reproducibility of the research presented in this paper, the methodology is freely available as an R-package downloadable from SolarClusGnr.

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Section: An Overview Of Literaturementioning

confidence: 99%

Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Frimane

Soubdhan²,

Bright

et al. 2019

Solar Energy

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“…Interestingly, a cluster analysis of the mean cloud coverage over Greece performed by Zagouras et al (2013Zagouras et al ( , 2014 suggests that the country can be divided into 22…”

Section: Hnse: CM Measurementsmentioning

confidence: 99%

Modeling the relationship between photosynthetically active radiation and global horizontal irradiance using singular spectrum analysis

Zempila

Taylor

Bais

et al. 2016

Journal of Quantitative Spectroscopy and Radiative Transfer

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“…Affinity Propagation (AP) [32] is an extensively used clustering algorithm that appears in a number of data engineering applications; recently, AP has been also used in solar data clustering [11]. The distinguishable characteristic of AP against other unsupervised clustering algorithms is the fact that it is not based on random initial selection of k centers like other centroid-based algorithms.…”

Section: Adaptive Affinity Propagationmentioning

confidence: 99%

“…To this end, a number of internal cluster validity indices (VI) have been proposed to estimate the quality of each cluster partition, in terms of compactness and separation of the obtained clusters [28]. The most well-cited VI have been extensively used in solar irradiance data clustering [9,10,11]. Research in these studies shows that the Calinski-Harabasz (CH) [36] VI performs smoothly and provides a monotonically decreasing evaluation graph as the NC increases.…”

Section: Cluster Validity Frameworkmentioning

confidence: 99%

“…In related studies, ground-based stations are members of existing sensor networks and they are used to define the ground-truth. Zagouras et al [9,10,11] were the first to use cluster analysis of features extracted from satellite-derived solar data to determine the appropriate number of coherent clusters within different domains of interest. The results of these studies were based on criteria related to the convergence of the clustering methods to cluster partitions, validated with clustering validity metrics.…”

Section: Introductionmentioning

confidence: 99%

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Objective framework for optimal distribution of solar irradiance monitoring networks

Zagouras

Kolovos²,

Coimbra

2015

Renewable Energy

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Time-resolved characterization of solar irradiance at the ground level is a critical element in solar energy analysis. Siting of nodes in a network of solar irradiance monitoring stations (MS) is a multi-faceted problem that directly affects the determination of the solar resource and its spatio-temporal variability. The present work proposes an objective framework to optimize the deployment of solar MS over a sub-continental region. There are two main components in the proposed methodology. The first employs cluster analysis using the affinity propagation algorithm, to select the optimal number of clusters (regions with coherent solar microclimates) upon internal coherence criteria. The second component employs stochastic prediction and validation, through the use of a Bayesian maximum entropy method, and selects the optimal MS configuration, according to geostatistical criteria, among the solutions recommended by the cluster analysis. We apply this two-pronged methodology to determine clusters and optimal locations for global horizontal irradiance monitoring across the state of California. In this proof-of-concept study, 3 disparate MS configurations are examined within the cluster partition. The subsequent geostatistical analysis indicates that all configurations rank almost equally well based on different statistical error measures. The optimal configuration can be singled out depending on desired criteria of choice.

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Clustering the solar resource for grid management in island mode

Cited by 23 publications

References 25 publications

Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Modeling the relationship between photosynthetically active radiation and global horizontal irradiance using singular spectrum analysis

Objective framework for optimal distribution of solar irradiance monitoring networks

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