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Technical Brief

URB-Solar: An Open-Source Tool for Solar Power Prediction in Urban Areas

[+] Author and Article Information
Venugopalan S. G. Raghavan

Fluid Dynamics Department,
Institute of High Performance Computing,
1 Fusionopolis Way #16-16, Connexis,
Singapore 138632

Harish Gopalan

Fluid Dynamics Department,
Institute of High Performance Computing,
1 Fusionopolis Way #16-16, Connexis,
Singapore 138632
e-mail: gopalanh@ihpc.a-star.edu.sg

1Corrsponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received November 8, 2017; final manuscript received June 20, 2018; published online July 24, 2018. Assoc. Editor: M. Keith Sharp.

J. Sol. Energy Eng 140(6), 064501 (Jul 24, 2018) (6 pages) Paper No: SOL-17-1447; doi: 10.1115/1.4040756 History: Received November 08, 2017; Revised June 20, 2018

The accurate prediction of the direct and diffuse solar radiation is of foremost importance for deployment of photovoltaic (PV) systems. A number of solar radiation forecasting techniques have been developed for longer and shorter forecasting times. Numerical weather prediction (NWP) models provide the best results for the longer forecasting times (4–6 h), required by utility companies. However, NWP methods are usually developed for clear-sky and open areas. These methods cannot be directly applied to urban areas with shading, trees, multisurface reflection, and other sources of solar radiation losses. To overcome these issues, improvement to the existing prediction tools are required. In this study, we develop an automated radiation forecasting tool for urban areas. This tool combines a NWP model (Weather Research and Forecasting (WRF) model) and a solar calculator (developed in the numerical toolbox OpenFOAM) to compute shading, reflection, and other losses in the urban canopy. An algorithm for extraction of building outlines and heights (if they are publicly available) is also developed as a part of the tool. Finally, the coupled solar power estimator can be applied to past, present, or future solar power predictions. Initial results obtained using the developed tool are demonstrated for an urban neighborhood in Singapore.

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Figures

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Fig. 1

Flowchart of work flow for the building geometry creation algorithm

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Fig. 2

Extraction of the building footprint from onemap images. All the buildings of the same color code have been extracted.

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Fig. 3

Flowchart of work flow for the tree extrusion or revolution algorithm

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Fig. 4

Creation of the CAD model of the tree from an image. The algorithm creates a two-dimensional representation of the tree. Extrusion or rotation of the two-dimensional object produces the three-dimensional tree model.

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Fig. 5

Comparison of direct and diffuse solar radiation (as a function of time) on different domains in WRF for Toa Payoh on Aug. 24, 2017. Simulations in the 1 × 1 km grid are nested within 3 × 3 km grid. The X-axis represents the hour of the day (in a 0-24 scale): (a) 3 × 3 km horizontal resolution and (b) 1 × 1 km horizontal resolution.

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Fig. 6

Comparison of direct solar radiation (in Wm−2) obtained from WRF simulation at 2 PM (Singapore standard time) on Aug. 24, 2017: (a) 3 × 3 km horizontal resolution and (b) 1 × 1 km horizontal resolution

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Fig. 7

Illustration of the solar load calculator: (a) different materials: buildings, trees, expressway and terrain and (b) contour plot of solar radiation forecast (in Wm−2) on different surfaces

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Fig. 8

Profiles of direct and diffuse radiation for an idealized day-time scenario for two extreme situations: (a) clear-sky and (b) complete cloud cover

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Fig. 9

Setup of the CAD model for the idealized resolution studies. Different albedo values (given in brackets) are assigned for the pavement (0.05), buildings (0.55), grass (0.25), and trees (0.15). Buildings A and B are raised above the ground to create a void-deck space: (a) geometry setup and (b) top view.

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Fig. 10

Shadow and shading effects for clear-sky and fully cloudy conditions. View is from the top: (a) clear-sky—coarse grid, (b) clear-sky—fine grid, (c) cloudy—coarse grid, and (d) cloudy—fine grid.

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