Classification of days according to DNI profiles using clustering techniques

Moreno-Tejera, Sara; Silva-Pérez, M.A.; Ramírez-Santigosa, L.; Lillo-Bravo, I.

doi:10.1016/j.solener.2017.02.031

Cited by 26 publications

(16 citation statements)

References 31 publications

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“…Solar irradiance profiles can be classified and grouped based on their irradiance level, the variability, and the distribution over the time of day [17]. Each feature can be characterized by its specific index.…”

Section: Determination Of Solar Collector Heat Outputmentioning

confidence: 99%

“…The variability index (VI) can be used to characterize the variability of solar irradiance. The VI is shown in Equation (4) and is defined as the ratio between the length of the solar irradiance profile and the length of the maximum enveloping clear sky solar irradiance profile [17]: where ∆t is the time interval in minutes, n is the number of time intervals of the day, and G cs is the clear sky solar irradiance. The solar irradiance in clear sky condition in Equations (3) and (4) can be calculated by the method of Hottel [19].…”

Section: Determination Of Solar Collector Heat Outputmentioning

confidence: 99%

“…The morning fraction (f m ) can be used to characterize the time distribution of solar irradiance profile in the day. The f m is defined as the ratio between the total solar irradiance from sunrise to solar noon and the total solar irradiance in the whole day, and it is determined using [17]:…”

Section: Determination Of Solar Collector Heat Outputmentioning

confidence: 99%

“…where G d,m is the measured solar irradiance from sunrise to the solar noon. The time fraction (f t ) can be used to segment morning fraction (f m ) into three groups for the clustering/grouping of solar irradiance profile, and the f t is given by [17]:…”

Section: Determination Of Solar Collector Heat Outputmentioning

confidence: 99%

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Time-Dependent Integration of Solar Thermal Technology in Industrial Processes

et al. 2020

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Solar energy is currently an underutilized renewable energy source that could fulfill low-temperature industrial heat demands with significant potential in high solar irradiance counties such as Malaysia. This study proposes a new systematic method for optimization of solar heat integration for different process options to minimize the levelized cost of heat by combining different methods from the literature. A case study from the literature is presented to demonstrate the proposed method combined with meteorological data in Malaysia. The method estimates capital cost and levelized cost of solar heating considering important physical constraints (e.g., available space) and recovery of waste heat. The method determines and optimizes important physical dimensions, including collector area, storage size, and control design. As the result of the case study, the solar thermal integration with Clean-In-Place streams (hot water) gives the lowest levelized cost of heat with RM 0.63/kWh (0.13 EUR/kWh) due to its lowest process temperature requirement. The sensitivity analysis indicates that collector price and collector efficiency are the critical parameters of solar thermal integration.

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Section: Determination Of Solar Collector Heat Outputmentioning

confidence: 99%

Section: Determination Of Solar Collector Heat Outputmentioning

confidence: 99%

Section: Determination Of Solar Collector Heat Outputmentioning

confidence: 99%

Section: Determination Of Solar Collector Heat Outputmentioning

confidence: 99%

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Time-Dependent Integration of Solar Thermal Technology in Industrial Processes

et al. 2020

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“…A comprehensive review of the common classification techniques used for solar irradiation and other alternative renewable energy sources can be found in Pérez-Ortiz et al (2016). For instance, Moreno-Tejera et al (2017) used the k-medoids algorithm to cluster days as a function of the sky state for concentrated solar power operation. Three indexes are used in this analysis, the transmittance index, persistence index of the instantaneous transmittance index values and variability index.…”

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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Assessment of characteristics of surface solar irradiance on consecutive days using a self‐organizing map and clustering methods

Watanabe

Oka

Hijioka

2021

Meteorological Applications

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The assessment of surface solar irradiance as a resource is a major research topic in renewable energy. A method for assessing the characteristics of global horizontal solar irradiance (GHI) at the surface on consecutive days is proposed in this work. A self-organizing map and cluster analysis are used as core analysis techniques in the proposed method. Using this method, the regional and seasonal characteristics of GHI for five consecutive days are analysed. One hour accumulated GHI data from ground observation stations in Japan are used. Time series assigned to each node on the topological map of the selforganizing map are characterized by strength and variability. The probabilities of occurrence of each node on the map differ between stations. Then, using the probabilities as a feature vector for quantifying the characteristics of each observation station, a cluster analysis is conducted to divide stations into clusters according to the dissimilarity of the features. The results show that there are three major regions in which the station characteristics are similar in Japan. Additionally, another cluster analysis is conducted to investigate the seasonal characteristics of the occurrence of time series patterns. It is found that consecutive cloudy days occur frequently in winter and during the rainy season, whereas consecutive clear days occur frequently in spring and summer.

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Classification of days according to DNI profiles using clustering techniques

Cited by 26 publications

References 31 publications

Time-Dependent Integration of Solar Thermal Technology in Industrial Processes

Time-Dependent Integration of Solar Thermal Technology in Industrial Processes

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

Assessment of characteristics of surface solar irradiance on consecutive days using a self‐organizing map and clustering methods

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