Modeling diffuse irradiance under arbitrary and homogeneous skies: Comparison and validation

Kocifaj, Miroslav; Kómar, Ladislav

doi:10.1016/j.apenergy.2016.01.024

Cited by 22 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, our conceptual data model can be a valuable contribution to the modelling community. For instance, a number of authors (Athanasiadis, 2013;Kosar et al, 2010;Degenne et al, 2009) have stressed that it is advantageous to programmers and modellers if the semantics of the language they use is near to their application domain. This is exactly what is provided by our data model: the modeller gets access to fields, agents and relations, and is capable of manipulating this information through a single conceptual representation of the data.…”

Section: Discussionmentioning

confidence: 99%

“…Software Applications Fields Longley et al (2011), Burrough and McDonnell (1998) MATLAB (MATLAB, 2012), PCRaster (Karssenberg et al, 2010;van Deursen, 1995), RasDaMan (Baumann et al, 1999) Hydrological models (Sutanudjaja et al, 2011), air pollution modelling (Hoek et al, 2008) Agents Grimm and Railsback (2013) GAMA (Grignard et al, 2014), RePast (North et al, 2013), TerraME (Carneiro et al, 2013), NetLogo (NetLogo, 2012), Mason/GeoMason (Sullivan et al, 2010) Economic modelling (Parker and Filatova, 2008), social simulation (Ziervogel et al, 2005) Agents with internal variation Clarke (2007) (deltatrons) no generic integrated software implementations known Cloud cover and solar radiation (Kocifaj and K omar, 2016), reflection of glaciers (Mernild et al, 2015), air pollution (Jjumba and Dragicevic, 2015), sediment modelling (Karssenberg and Bridge, 2008), tree growth (Valentine and M€ akel€ a, 2005) Networks Galton and Worboys (2005), Goodrich and Tamassia (2002) graph-tool (Peixoto, 2014), R (R Core Team, 2013), NetworkX (Hagberg et al, 2008) Farm/household interactions (Berger, 2001) Integration of fields and agents Ferreira et al (2014), OGC (2011), Voudouris (2010), Liu et al (2008), Moreno et al (2008), Goodchild et al (2007), Kjenstad (2006), Cova and Goodchild (2002), Câmara et al (2000) TerraME (Carneiro et al, 2013) Air pollution (Jjumba and Dragicevic, 2015), animals and environment (Schippers et al, 2014), ...…”

Section: Conceptual Modelmentioning

confidence: 99%

“…Other examples include channel belts (Karssenberg and Bridge, 2008), spatial variation of albedo over glaciers (Mernild et al, 2015) or solar energy models simulating heterogeneous cloud covers and their effect on solar radiation diffusion (e.g. Kocifaj and K omar 2016). These examples reveal that systems that can be represented as agents often also have properties that vary continuously within the spatio-temporal extent associated with each agent.…”

Section: Agents With Internal Variationmentioning

confidence: 99%

“…Karssenberg et al, 2010), which provides a programming interface matching the conceptual level of thinking of domain specialists. The building blocks of models can be offered in a generic scripting language or, in its most sophisticated form, as a Domain-Specific Language (DSL) (Mernik et al, 2005;van Deursen et al, 2000), which is a language that matches the semantics of a specific domain of use, improving productivity, verification and optimization (Kosar et al, 2010;Fisher, 1999). This approach of using model building software frameworks is promising because it becomes easier for domain specialists to program models (Holst and Belete, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Design and demonstration of a data model to integrate agent-based and field-based modelling

Bakker

Jong

Schmitz

et al. 2017

Environmental Modelling & Software

View full text Add to dashboard Cite

Dynamic environmental modelling of spatio-temporal systems often requires the representation of both fields and agents. Fields are continuous with values in the whole spatio-temporal domain of a model, while agents are bounded in space and often mobile. It is currently difficult for environmental modellers with limited software engineering background to construct such field-agent models, as modelling frameworks mostly do not support the integration of fields and agents. To overcome this issue, we describe a data model combining fields and agents in a single concept. This data model represents fields, agents and relations by grouping items sharing properties into a phenomenon. The concepts domain, property set and value handle spatio-temporal attribute representations. The data model is implemented in a software prototype that shows how data on fields and agents is stored and manipulated.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Conceptual Modelmentioning

confidence: 99%

Section: Agents With Internal Variationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design and demonstration of a data model to integrate agent-based and field-based modelling

Bakker

Jong

Schmitz

et al. 2017

Environmental Modelling & Software

View full text Add to dashboard Cite

show abstract

“…It is our intention to inspect the range of errors induced in this approximation. To do this, we compare the exact computations based on the UMRP (Unified Model of Radiative Patterns) of heterogeneous cloud fields [27] against a well-known all-weather model of homogeneous skies [19]. Note that Perez's model is commonly used in many applications and is designed to predict the sky luminance from the global and direct irradiance data and dew point temperature.…”

Section: Comparison Of Light-tube Optical Properties Under Arbitrary mentioning

confidence: 99%

An Accurate Prediction of Daylight Pipe Harvesting of Interior Space

Kómar

Kocifaj

2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Featured Application: The results of this research are useful in correct assessment of daylighting quality in the interiors respecting the typical instability of sky states. The computations we have performed allow for designing the optimum energy-saving solutions for light-pipe systems. Abstract:The performance of a light guide under arbitrary meteorological conditions is difficult to predict numerically because none of the present methods can account for the random configuration of clouds. Although, partly cloudy skies typically occur very frequently in many regions over the globe, the effects of cloud sizes, distributions, or altitudes on the luminous effectiveness of a light guide remain virtually unknown. The physical installation of light guides does not allow for a controlled experiment in which a single parameter can be varied within a defined range, while holding all other parameters constant. Numerical modeling is the only practical way to provide deep insights on the role of a specific property, such as cloud coverage, and its importance relative to all others (e.g., atmospheric turbidity). We have developed a unique solution through linking the UniSky simulator and HOLIGILM (hollow light guide interior illumination method) tool, while providing for the first time a powerful method that can provide accurate predictions and understanding of the fundamental differences of light guide behaviors under homogeneous and inhomogeneous sky states. The use of homogeneous skies in low-accuracy empirical models often results in overestimated/underestimated effectiveness of some light-guide systems. The model we have developed can predict complex optical signatures that are normally impossible to reproduce using up-to date empirical models (e.g., the illuminance patterns due to isolated clouds or clouds traversing across the sky). The optical efficiency, average cosine and working-place illuminance are analyzed and compared in order to demonstrate the model capabilities.

show abstract

Prediction of diffuse photosynthetically active radiation using different soft computing techniques

Wang

Kisi

et al. 2017

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Knowledge of diffuse photosynthetically active radiation (PARd) is important for many applications dealing with climate change, environmental engineering and terrestrial productivity. It is necessary to estimate the PARd using different techniques due to the absence of direct observations of this radiometric flux in most parts of the world. In this study, Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) with grid partition (ANFIS‐GP), ANFIS with subtractive clustering (ANFIS‐SC) and M5 model tree (M5Tree) are optimized and applied for predicting hourly PARd in different ecosystems. Hourly climatic variables at six stations from the AmeriFlux network are used for training, validating and testing the above models. It is observed that the mean bias error (MBE) values range −11 to +18%, −16 to +9% and −10 to +9% for ANFIS‐GP, ANFIS‐SC and M5Tree models, respectively; root mean square errors (RMSE) range 25–44%, 22–41% and 29–51%, respectively for different stations in the testing period. The results show that the ANFIS‐SC model can generally bring more accurate estimations than other models, and the statistical indices (MBE and RMSE) at CA_Gro stations (mixed forests) are lower than other stations for each model. The models underestimate some high PARd values (>900 µmol m−2 s−1) for some stations, which may due to the differences of data ranges and distributions. This study will lay the foundation for accurately mapping regional and global distributions of PARd and its associated ecological applications.

show abstract

Modeling diffuse irradiance under arbitrary and homogeneous skies: Comparison and validation

Cited by 22 publications

References 43 publications

Design and demonstration of a data model to integrate agent-based and field-based modelling

Design and demonstration of a data model to integrate agent-based and field-based modelling

An Accurate Prediction of Daylight Pipe Harvesting of Interior Space

Prediction of diffuse photosynthetically active radiation using different soft computing techniques

Contact Info

Product

Resources

About