scholarly journals 3D Solar Photovoltaic Community Energy Modeling

2021 ◽  
Author(s):  
Annie Chow
Keyword(s):  
Solar Energy ◽  
Software Tools ◽  
Measured Data ◽  
Computational Time ◽  
Solar Pv ◽  
3D Gis ◽  
Energy Potential ◽  
Gis Modeling ◽  
Different Types

<div>The aim of this research is to increase the assessment ability of solar energy utilization and planning support for clusters of different types of buildings in a mixed-use community. Particular focus will be placed on the analysis of community-based modeling, mapping and forecasting of solar potentials on the rooftops of buildings. New systems and methodologies with appropriate level of detail at a lower computational time are needed to accurately model, estimate and map solar energy potential at a high spatiotemporal resolution. To accomplish this goal and to develop an integrated solution, the assessment ability was investigated using two different types of studies: (1) 3D GIS modeling of a solar energy community, and (2) benchmarking of solar PV radiation software tools. A 3D GIS modeling and mapping approach was developed to assess community solar energy potential. A model was created in ESRI ArcGIS, to efficiently compute and iterate the hourly solar modeling and mapping process over a simulated year. The methodology was tested on a case study area located in southern Ontario, where two different 3D models of the site plan were analyzed. The accuracy of the work depended on the resolution and sky size of the input model. An assessment of solar simulation software tools was performed to evaluate their strengths and weaknesses for performing analysis in the PV modeling process. The software tools assessed were HelioScope, PVsyst, PV*SOL,</div><div>Archelios, EnergyPlus, and System Advisor Model (SAM). The performance of the software tools were assessed based upon their accuracy in simulation performance against measured data, and the comparison of their physical functions and capabilities. A case study near London, Ontario with an 8.745kWp PV system installation was selected for analysis, and EnergyPlus was found to have predictions closest to measured data, ranging from -0.6% to 3.6% accuracy. Based upon the GIS study and the evaluation of the six solar software tools, recommendations for the development of a future application to couple GIS with the internal submodels of the software tools were made to create the ideal tool for 3D modeling and mapping of solar PV potential. </div>

scholarly journals 3D Solar Photovoltaic Community Energy Modeling

2021 ◽  
Author(s):  
Annie Chow
Keyword(s):  
Solar Energy ◽  
Software Tools ◽  
Measured Data ◽  
Computational Time ◽  
Solar Pv ◽  
3D Gis ◽  
Energy Potential ◽  
Gis Modeling ◽  
Different Types

<div>The aim of this research is to increase the assessment ability of solar energy utilization and planning support for clusters of different types of buildings in a mixed-use community. Particular focus will be placed on the analysis of community-based modeling, mapping and forecasting of solar potentials on the rooftops of buildings. New systems and methodologies with appropriate level of detail at a lower computational time are needed to accurately model, estimate and map solar energy potential at a high spatiotemporal resolution. To accomplish this goal and to develop an integrated solution, the assessment ability was investigated using two different types of studies: (1) 3D GIS modeling of a solar energy community, and (2) benchmarking of solar PV radiation software tools. A 3D GIS modeling and mapping approach was developed to assess community solar energy potential. A model was created in ESRI ArcGIS, to efficiently compute and iterate the hourly solar modeling and mapping process over a simulated year. The methodology was tested on a case study area located in southern Ontario, where two different 3D models of the site plan were analyzed. The accuracy of the work depended on the resolution and sky size of the input model. An assessment of solar simulation software tools was performed to evaluate their strengths and weaknesses for performing analysis in the PV modeling process. The software tools assessed were HelioScope, PVsyst, PV*SOL,</div><div>Archelios, EnergyPlus, and System Advisor Model (SAM). The performance of the software tools were assessed based upon their accuracy in simulation performance against measured data, and the comparison of their physical functions and capabilities. A case study near London, Ontario with an 8.745kWp PV system installation was selected for analysis, and EnergyPlus was found to have predictions closest to measured data, ranging from -0.6% to 3.6% accuracy. Based upon the GIS study and the evaluation of the six solar software tools, recommendations for the development of a future application to couple GIS with the internal submodels of the software tools were made to create the ideal tool for 3D modeling and mapping of solar PV potential. </div>

scholarly journals Mapping and Estimation of Monthly Global Solar Irradiation in Different Zones in Souss-Massa Area, Morocco, Using Artificial Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
O. Nait Mensour ◽  
S. Bouaddi ◽  
B. Abnay ◽  
B. Hlimi ◽  
A. Ihlal
Keyword(s):  
Solar Energy ◽  
Solar Irradiation ◽  
Kriging Interpolation ◽  
Solar Pv ◽  
Energy Potential ◽  
Global Solar Irradiation ◽  
Interpolation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Solar radiation data play an important role in solar energy research. However, in regions where the meteorological stations providing these data are unavailable, strong mapping and estimation models are needed. For this reason, we have developed a model based on artificial neural network (ANN) with a multilayer perceptron (MLP) technique to estimate the monthly average global solar irradiation of the Souss-Massa area (located in the southwest of Morocco). In this study, we have used a large database provided by NASA geosatellite database during the period from 1996 to 2005. After testing several models, we concluded that the best model has 25 nodes in the hidden layer and results in a minimum root mean square error (RMSE) equal to 0.234. Furthermore, almost a perfect correlation coefficient R=0.988 was found between measured and estimated values. This developed model was used to map the monthly solar energy potential of the Souss-Massa area during a year as estimated by the ANN and designed with the Kriging interpolation technique. By comparing the annual average solar irradiation between three selected sites in Souss-Massa, as estimated by our model, and six European locations where large solar PV plants are deployed, it is apparent that the Souss-Massa area is blessed with higher solar potential.

scholarly journals Deep learning-based estimation of PV power plant potential under climate change: a case study of El Akarit, Tunisia

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Afef Ben Othman ◽  
Ayoub Ouni ◽  
Mongi Besbes
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Assessment Tools ◽  
Solar Pv ◽  
Energy Potential ◽  
Study Region ◽  
Energy Field ◽  
Future Production

Abstract Background Several climatologists and experts in the renewable energy field agree that GHI and DNI calculation models must be revised because of the increasingly unpredictable and powerful climatic disturbances. The construction of analytical mathematical models for the prediction of these disturbances is almost impossible because the physical phenomena relating to the climate are often complex. We raise the question over the current and future PV system’s sustainable energy production and whether climate disturbances will be affecting this sustainability and resulting in supply decline. Methods In this paper, we tried to use deep learning as a tool to predict the evolution of the future production of any geographic site. This approach can allow for improvements in decision-making concerning the implantation of solar PV or CSP plants. To reach this aim, we have deployed the databases of NASA and the Tunisian National Institute of Meteorology relating to the climatic parameters of the case study region of El Akarit, Gabes, Tunisia. In spite of the colossal amount of processed data that dates back to 1985, the use of deep learning algorithms allowed for the validation of the previously made estimates of the energy potential in the studied region. Results The calculation results suggested an increase in production as it was confirmed by the 2019 measures. The findings obtained from the case study region were reliable and seemed to be very promising. The results obtained using deep learning algorithms were similar to those produced by conventional calculation methods. However, while conventional approaches based on measurements obtained using hardware solutions (ground sensors) are expensive and very difficult to implement, the suggested new approach is cheaper and more convenient. Conclusions In the existence of a protracted controversy over the hypothetical effects of climate change, making advances in artificial intelligence and using new deep learning algorithms are critical procedures to strengthening conventional assessment tools of the production sites of photovoltaic energy and CSP plants.

scholarly journals Modeling Urban Solar Energy with High Spatiotemporal Resolution

2021 ◽  
Author(s):  
Annie Chow
Keyword(s):  
Solar Energy ◽  
Fossil Fuels ◽  
Energy Resource ◽  
Electricity Consumption ◽  
Software Tool ◽  
Solar Pv ◽  
Spatiotemporal Resolution ◽  
High Spatiotemporal Resolution ◽  
Case Study Area

Alternative sources of energy are being sought after in the world today, as the availability of fossil fuels and other non-renewable resources are declining. Solar energy offers a promising solution to this search as it is a less polluting renewable energy resource and can be easily converted into electricity through the usage of photovoltaic systems. This thesis focuses on the modeling of urban solar energy with high spatiotemporal resolution. A methodology was developed to estimate hourly solar PV electricity generation potential on rooftops in an urban environment using a 3-D model. A case study area of Ryerson University, Toronto was chosen and the incident solar radiation upon each building rooftop was calculated using a software tool called Ecotect Analysis 2011. Secondly, orthophotos of the case study area were digitized using Geographic Information Systems in order to eliminate undesirable rooftop objects within the model. Lastly, a software tool called HOMER was used to generate hourly solar PV electricity estimates using the values generated by the other two software tools as input parameters. It was found that hourly solar PV output followed the pattern of a binomial curve and that peak solar generation times coincided with summer peak electricity consumption hours in Ontario.

RESIDENTIAL SOLAR ENERGY POTENTIAL FOR PUBLIC DISSEMINATION: A CASE STUDY IN CONCEPCIÓN, CHILE

2016 ◽  
Vol 11 (1) ◽  
pp. 118-133 ◽  
Author(s):  
Rodrigo García Alvarado ◽  
Lorena Troncoso ◽  
Pablo Campos
Keyword(s):  
Solar Energy ◽  
Energy Demand ◽  
Geographical Location ◽  
Average Energy ◽  
Aerial Photographs ◽  
Energy Potential ◽  
Technical Standards ◽  
Dynamic Simulations ◽  
Residential Units

This paper presents a method for estimating the solar capture capacity of dwellings using the central urban area of Concepción, Chile, as a case study in order to promote self-generation of energy by residents. The method takes into account the growing domestic energy demand and the possibility of meeting this demand through integrated solar energy collection into buildings using different systems. The methodology considers a study of the potential incoming solar radiation on buildings according to their geographical location and the surrounding buildings. The capacity for solar capture is then estimated for different dwelling types according to their morphology. Subsequently, the energy contribution provided by different technologies (solar thermal, photovoltaic and hybrid) is identified in relation to the main average energy demands for electricity, water and space heating. Finally, systems for each dwelling are recommended in an urban map available online. The development is based on climate information, cartography, aerial photographs, surveys, housing models, technical standards, standardised calculations and dynamic simulations, implemented according to building layouts from an online Geographic Information System (GIS). The housing types are categorised in an urban map that relates household demands and the contribution of different solar energy systems. According to the estimates calculated, the residential units in the study offer sufficient solar capacity to supply between 40 and 60% of their energy consumption, especially in detached houses using roof-mounted hybrid systems.

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