Today’s global energy demand still relies largely on fossil fuels [1
]. This results in rapid increase in anthropogenic greenhouse gas emissions and overexploitation of natural resources. The issues of global change, energy crisis, and environmental pollution caused by burning fossil fuels have drawn worldwide attention [2
]. The pursuit of new renewable energy, such as solar, wind, biomass, nuclear, and hydropower, as substitutions for traditional fossil fuels is an urgent need [2
]. As a clean and renewable energy source, solar energy has great potential because of its flexibility and sustainability [4
]. It was reported that solar power generation grew rapidly (+58%) in the world in 2012, which is higher than the average growth rate of renewable energy used in power generation (15.2%) [5
]. Solar energy is harnessed by using several types of technologies, such as solar heating, solar photovoltaic, and solar photochemistry. Common urban applications include the solar thermal collector for water heating and the solar photovoltaic panel placed on building roofs [6
]. The estimation of the potential solar radiation on building roofs is significantly important for improving the efficiency of solar energy utilization in the urban environment.
The potential of solar energy utilization at a specific site in urban areas, especially in downtown areas, is determined by three factors: the amount of solar radiation reaching the ground, the accessibility of the receiving solar radiation at a specific location, and the space available for mounting the solar instruments. The amount and intensity of the shortwave solar radiation at ground level typically vary with geographic location (latitude), season of year, time of day, atmospheric condition (e.g., cloud coverage, atmospheric transparency), and original Earth surface topography [8
]. Particularly for urban areas, the emergence of various artificial objects and natural features creates complex urban morphology, which has profound influence on the sunlight, shade, and solar radiation access within urban space [10
]. Thus, the spatial distribution of solar radiation receiving surface at a given time and season is largely influenced by the complex urban morphology. In addition, the very limited space in urban environment also affects the space available for mounting solar instruments [12
]. Therefore, to fully delineate and quantify the practical solar radiation in the urban area, an assessment framework needs to include a solar radiation simulation model that deals with various sky conditions, detailed and accurate urban morphological information, as well as the function to identify potential spaces where solar instruments can most utilize the received solar radiation.
Some spatially explicit solar flux models have been developed to simulate the topographical effects on solar radiation variations over time and space (e.g., [8
]). Most of them were designed to model the spatio-temporal variations of solar radiation in natural mountainous terrains based on coarse resolution Digital Elevation Models (DEMs) [8
]. However, coarse resolution surface topography data are unable to support a reliable and detailed analysis of urban 3D morphology. Besides, high-resolution surface topography data is still unavailable for most metropolitan areas. Thus, in previous studies, there has not been much discussion on the simulation of solar radiation in the urban built-up area. In the recent decade, digital photogrammetry and airborne Light Detection and Ranging (LiDAR) Remote Sensing technology have provided highly accurate and densely sampled topographic measurements for surface morphology surveying and mapping in urban environments. Photogrammetry requires true-orthophoto generation from aerial photographs, and the produced surface elevation largely depends on the geometrical resolution and the quality of the aerial images [15
]. In contrast, surface elevation measurements obtained from airborne LiDAR are quicker and more effective than traditional photogrammetric techniques [17
]. Since then, a number of studies have been conducted using LiDAR data on the urban 3D building modeling [19
], urban vegetation identification and green volume estimation [24
], and solar potential evaluation in the urban area [26
]. For example, Yu, et al.
] demonstrated that the integration of urban Digital Surface Model (DSM) from LiDAR data and a solar radiation flux model is able to investigate the spatio-temporal variations of solar radiation in downtown Houston. Their study provided a new effective and efficient way to model the impacts of urban 3D morphology on solar radiation in an urban built-up environment. Santos, et al.
] estimated solar radiation on rooftops by employing GIS-based solar models and LiDAR data, and assessed the photovoltaic potential of residential buildings in Lisbon, Portugal. Despite valuable findings, these previous studies only focused on modeling solar radiation under a clear-sky condition although the atmospheric condition has a significant attenuation influence on solar radiation transmission [31
]. It has been shown that solar radiation transmission through the atmosphere is normally attenuated by atmospheric turbidity and cloud cover [32
]. Turbidity attenuation is caused by aerosols and atmospheric gases, which are highly variable in both time and space and often infeasible to be measured at the desired resolution for solar radiation simulation at urban scale [33
]. For cloud cover, this has the greatest influence on ground level irradiance attenuation [31
], particularly for coastal or tropical urban areas. Thanks to the development in satellite technology, cloud cover at urban level can now be monitored successfully using meteorological satellite data, such as FengYun-2F. FengYun-2F is the fourth geostationary meteorological satellite operated by China and was launched on 13 January 2012. It provides a visible image and four infrared images hourly in normal periods and every 30 min in the flood season. By utilizing FengYun data, cloud influence can be determined and integrated into the clear-sky solar flux model for handling different sky conditions.
In addition, as high-resolution airborne LiDAR data is employed to represent complex urban 3D morphology, high-performance computation techniques are required for fast and accurate solar radiation estimation. A number of high performance geospatial computation approaches have been taken to process large remote sensing data, such as cluster computing and hardware-based acceleration [34
]. However, applications of computer clusters are largely restricted by their high cost. The use of a parallel hardware structure makes high-performance computation possible for general purposes, and various techniques have been developed, among which is the GPU (Graphic Processing Unit) technique that has been increasingly popular in parallel computation. GPU is initially designed to work with CPU (Central Processing Unit) to speed up graphic rendering, and now it can also be employed as a parallel structure for computation due to its unique design feature [35
]. Normally a GPU has a much greater number of cores than a CPU, which means that the memory access latency can almost be ignored when running a large number of threads simultaneously [36
]. Therefore, although the clock rate of a single GPU core is lower than that of a single CPU core, the overall efficiency of parallel computation on GPU can be much higher. There are two main GPU programming architectures available so far, namely NVIDIA’s Compute Unified Device Architecture (CUDA) and ATI’s Close To the Metal (CTM). Hu, et al.
] compared the efficiency of fast filtering of LiDAR data using the Open Multi-Processing (OpenMP) method, a technology of multiprocessing in CPU, and NVIDIA’s CUDA, and found that CUDA can increase computational efficiency to a much larger extent when compared with OpenMP. CUDA has also been used for solar radiation simulation. For example, Lukač and Žalik [37
] employed CUDA and used LiDAR data to estimate the roof’s direct and diffuse solar radiance potential in two test locations in Slovenia. A multiresolutional shadowing approach was taken where the urban area was treated with higher resolution and the surrounding hilly area with lower resolution. The influence of the vegetation was also considered. Their results show that the GPU-based CUDA is faster and more efficient compared with the multi-core CPU approach for solar irradiance computation. To our best knowledge, no research has been reported to simulate the solar radiation (including direct, reflect, and diffuse radiation) under various cloudy conditions using a GPU-accelerated method.
The objective of this paper was two-fold. Firstly, by integrating high-resolution airborne LiDAR data and FengYun meteorological data, we proposed a new GPU-based solar radiation model, named SHORTWAVE-C, which is updated from the SHORTWAVE model developed by Kumar, et al.
], to evaluate solar radiation intensity under cloudy condition. The GPU acceleration method ensures that the simulation of solar radiation in large spatial scale is practically feasible. Secondly, in terms of practical application, an object-based method was adopted to segment building roofs and identify suitable roofs for enhancing the utilization of solar energy. The next sections are organized as follows. Section 2
introduces the case study area and data collection method. In Section 3
, the GPU-based solar radiation model and the object-based method for selecting suitable building roofs are described in detail. In Section 4
, the spatio-temporal pattern of solar radiation, efficiency of GPU-accelerated estimation, and selection of suitable roofs are analyzed and discussed. The last section draws some conclusions.
Solar energy is becoming increasingly important as an alternative to traditional fossil fuel to deal with the worldwide energy crisis and environmental pollution. The potential of solar energy utilization in urban areas is largely influenced by solar position, atmospheric influence, complex urban 3D morphology, and the space available for mounting solar instruments. Thus, an efficient and accurate method to estimate solar radiation and select suitable places to install solar panels is fundamental in the urban environment. In this study, we proposed a GPU-based solar radiation model, named SHORTWAVE-C, to simulate solar radiation under various sky conditions. Cloud influence acquired from FengYun-2F meteorological satellite data was integrated into our solar flux model. The GPU acceleration method was utilized to improve the solar radiation calculation. Besides that, an object-based method was adopted to locate suitable roof planes for enhancing utilization of solar energy.
By using airborne LiDAR data along with hourly cloud amount data from FengYun-2F, we simulated direct, diffuse, and reflected solar radiation as well as the solar illumination duration in the Lujiazui region with consideration of cloud influence. Then we summarized monthly, seasonal, and yearly solar radiation, and compared our results with the radiation amount calculated under clear-sky condition. The monthly average total solar radiation shows a relatively large variation from January to December, with the highest radiation intensity in July (21.8 MJ/m2/day) and the lowest intensity in December (6.7 MJ/m2/day). Direct radiation is the predominant component over a year, contributing more than 85% to the total solar radiation. Since we took cloud amount into account, our estimation presents relatively lower total solar radiation compared with results under clear-sky condition, especially in spring and summer. In addition, complex urban 3D morphology plays an important role on the spatial distribution of solar radiation in the downtown area. Open space like streets and open plots can receive more direct sunlight, showing higher total solar radiation than other spaces. Since the skyscrapers and high-rise buildings obstruct sunlight and cause long shades and shadows, the street canyons among high-rise buildings often show relatively lower solar radiation, particularly in winter when the sun is located at a lower solar altitude angle. The solar radiation intensity on skyscraper rooftops and residential building rooftops is different. Due to the complex rooftop structure and large height difference among surrounding tall buildings, skyscrapers receive less direct sun light and shorter hours of solar illumination than residential buildings. However, they account for more than 6.1% reflected radiance compared with traditional buildings.
In our research, the GPU acceleration has proved to be a good solution to enhance the efficiency of solar radiation estimation. Using CUDA is able to save more computation time as interval time decreases. The speedup ratio reaches up to 46% when we use 10 min as interval time in our case. The results also show that the speedup ratio grows quickly if the interval time is larger than 60 min, but keeps steady (45%–46%) when the time step is less than 60 min.
An object-based method was adopted to segment roof planes and locate suitable planes for enhancing utilization of solar energy. In terms of suitable roof plane area, slope, aspect, total solar radiation, and solar illumination duration, 1729 roof planes were identified in the Lujiazui region. The spatial distribution of suitable locations for solar planes shows that the desirable positions are mostly located on the sunward roof planes in residential districts. The yearly average total solar radiation of those roof planes is more than 16 MJ/m2/day. There are less suitable roof planes on skyscrapers because of the complex rooftop structure and lower solar radiation received on their rooftops. This study provides useful strategic guidelines for urban energy planning and sustainable development.
There are still some limitations in our study. FengYun-2F data have been collected since 2012, while our LiDAR data was collected in 2006. So we have to use the DSM in 2006 to simulate solar radiation in 2013, which might have some differences to the real elevation information in 2013. Besides that, although our GPU-based SHORTWAVE-C model is effective and efficient, it called the hillshade analysis function from ArcGIS for each time interval, which takes a much longer time than directly processing in GPU. In addition, we only employed five threshold-based criteria to select the suitable roofs for utilization of solar panels. In future research, we will try to adopt other algorithms for identifying desirable roof planes, such as machine learning and object-based classification.