Assessment Of Solar Potential Research Articles

High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning

Spatiotemporal assessment of solar potential is one of the most promising solutions to find suitable locations for future photovoltaic systems’ placement. However, accurate assessment of solar potential on large areas can be challenging due to missing data or computational complexity. In this paper, a fully convolutional neural network based method for high-resolution spatiotemporal assessment of solar potential by using remote sensing data is presented. The method is trained and validated on the area of 32 km2 of the Maribor city, Slovenia, and tested on the 6 different locations in Slovenia and Germany. The proposed method was tested against the simulation algorithm, which utilized isotropic Liu Jordan diffuse model or anisotropic Perez model with sky view factor based shading. On average, a normalized root mean square error (NRMSE) of 6.06% and mean absolute percentage error (MAPE) of 4.29% was achieved in test locations against the simulation based on Liu-Jordan model. When training the fully convolutional neural network models against the ground truth, generated with more advanced Perez diffuse model, the average NRMSE was 8.37% and MAPE of 10.90% was achieved across all test locations. Additionally, it was shown that the proposed method can assess solar potential in high-resolution from the input in lower resolution with higher accuracy than the simulation algorithm, while being up to 1500-times faster.

An Integrated Approach for 3D Solar Potential Assessment at the City Scale

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. Assessing the shadows cast by nearby buildings and vegetation is essential, especially at the city scale. Due to urban complexity, conventional methods using Digital Surface Models (DSM) overestimate solar irradiation in dense urban environments. To provide further insights into this dilemma, a new modeling technique was developed for integrated 3D city modeling and solar potential assessment on building roofs using light detection and ranging (LiDAR) data. The methodology used hotspot analysis to validate the workflow in both site and without-site contexts (e.g., trees that shield small buildings). Field testing was conducted, covering a total area of 4975 square miles and 10,489 existing buildings. The results demonstrate a considerable impact of large, dense trees on the solar irradiation received by smaller buildings. Considering the site’s context, a mean annual solar estimate of 99.97 kWh/m2/year was determined. Without considering the site context, this value increased by 9.3% (as a percentage of total rooftops) to 109.17 kWh/m2/year, with a peak in July and troughs in December and January. The study suggests that both factors have a substantial impact on solar potential estimations, emphasizing the importance of carefully considering the shadowing effect during PV panel installation. The research findings reveal that 1517 buildings in the downtown area of Austin have high estimated radiation ranging from 4.7 to 6.9 kWh/m2/day, providing valuable insights for the identification of optimal locations highly suitable for PV installation. Additionally, this methodology can be generalized to other cities, addressing the broader demand for renewable energy solutions.

Estimating surface utilization factors for BIPV applications using pix2pix on street captured façade images

While techniques for assessing solar potential, particularly on roofs, are well-established, estimating solar potential on building facades often requires more work due to the complexity of urban features and the elaborate design of building facades. Existing methods to assess the solar potential of building facades often neglect the characteristics of individual facades. This study presents an image-based method for a more accurate estimation of the PV potential of facades. The proposed method is composed of four steps: (1) data acquisition, preprocessing, and manual labeling, (2) training a pixel-wise semantic image segmentation model based on Generative Adversarial Networks (GANs), (3) color content analysis of segmented images, and (4) estimating the annual solar energy potential. We apply the proposed workflow to several buildings in Zurich, Switzerland, and evaluate segmentation quality and resulting changes in façade surface utilization factors. In a comparative analysis between the widely used web-based solar potential assessment tools, Sonnenfassade, Global Solar Atlas and the simulation software Climate Studio, we demonstrate the downstream impact of surface utilization factors on resulting solar potentials. Considerable deviations in façade availability for BIPV deployments among the various tools as compared to our segmentation approach indicate the potential impact of the proposed method on policy-making and the benefits for BIPV design and planning.

Evaluation of Critical Solar PV Meteorological and Performance Parameters of a Roof-Mounted Crystalline Solar PV System in Berea, Durban, South Africa

The technical challenges that make solar photovoltaic (PV) less suitable for some applications include weather conditions, low energy densities, and low conversion efficiency. However, the effects of these shortcomings can be limited through a 3-stage solar PV system assessment and performance evaluation process in this study. The system installation is on geographical coordinate -29.85°, 031.00° at Musgrave, Berea, Durban, South Africa. Four solar PV assessment, design, and simulation software applications (PV*SOL, SOLARGIS Prospect and pvPlanner, and PVsyst) were deployed. Based on the simulation reports obtained from the four software applications used, the following estimates of solar potential assessment parameters were obtained - global tilted irradiation GTI, (1890 kWh/m2); global horizontal irradiation, GHI (1684 kWh/m2); diffuse horizontal irradiation DIF, (694 kWh/m2); ambient temperature (19 °C). In addition, the following performance parameters were reported - produced energy (13.06 MWh/year); specific production (1511 kWh/kWp/year); performance ratio, PR (79 %); and solar fraction, SF (36.92 %).

Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus

Achieving sustainability through solar energy has become an increasingly accessible option in the United States (US). Nationwide, universities are at the forefront of energy efficiency and renewable generation goals. The aim of this study was to determine the suitability for the installation of photovoltaic (PV) systems based on their solar potential and corresponding electricity generation potential on a southern US university campus. Using Auburn University located in the southern US as a case study, freely available geospatial data were utilized, and geographic information system (GIS) approaches were applied to characterize solar potential across the 1875-acre campus. Airborne light detection and ranging (lidar) point clouds were processed to extract a digital surface model (DSM), from which slope and aspect were derived. The area and total solar radiation of campus buildings were calculated, and suitable buildings were then determined based on slope, aspect, and total solar radiation. Results highlighted that of 443 buildings, 323 were fit for solar arrays, and these selected rooftops can produce 27,068,555 kWh annually. This study demonstrated that Auburn University could benefit from rooftop solar arrays, and the proposed arrays would account for approximately 21.07% of annual electricity requirement by buildings, equivalent to 14.43% of total campus electricity for all operations. Given increasing open and free access to high-resolution lidar data across the US, methods from this study are adaptable to institutions nationwide, for the development of a comprehensive assessment of solar potential, toward meeting campus energy goals.

Adapted strategy for large-scale assessment of solar potential on facades in urban areas focusing on the reflection component

The objective of this paper is to propose an adapted method of calculation to evaluate the solar potential on building facades on a large scale. Autonomous electricity supply is nowadays an important issue, particularly during the winter. Thus the facades of buildings, which are better irradiated in winter than the roofs, can provide a response to this issue. Research has been focusing for several years on fine modeling of radiative phenomena in an urban environment. The first challenge is to obtain a physically realistic model considering the phenomena of local reflections. The second challenge is to succeed in implementing this type of simulations on large scales. These two issues are antagonistic: the first implies making the model more complex while the second implies simplifying it. This paper proposes a calculation strategy to reconcile the two issues based on a simplified radiosity method. The results obtained via this strategy were compared to those obtained by measurement and simulation on a theoretical canyon. Subsequently, this calculation strategy has been deployed to real districts in Geneva, Switzerland.

Hemispherical Photographs: A Review of Acquisition Methods and Applications in the Context of Urban Energy and Environment Assessments

Abstract From the use of a pinhole camera placed under a water tank, which was proposed almost 100 years ago, to the application of modern digital cameras mounted with sophisticated fisheye lenses, acquisition methods for capturing hemispherical photographs undergone vigorous research and development. Over the past few decades, such photographs have been extensively used in evaluating energy and environmental aspects in urban contexts. In this review article, the advantages, limitations, and challenges of the various methods of acquiring photographs are described and compared. This involves both the devices themselves and the software tools. Several methods of direct acquisition of hemispheric photographs involving digital cameras, smartphones, the use of drones for photographs at elevations, and the application of thermal imaging technologies are discussed in detail. Indirect methods for generating hemispheric photographs are also discussed, highlighting the use of images from applications such as Google Street View (GSV). Based on a review of technical literature, several applications in energy and the environment that use information from hemispheric photographs as an analysis tool are presented and discussed. Among others, the following are discussed: the quantification of solar radiation potential; the assessment of indicators of local temperature and level of thermal comfort for pedestrians in urban areas; indoor and outdoor daylighting; and air and light pollution. Finally, several potential future research directions for the use of hemispherical photographs in built environments are discussed. These include advances in image processing, use of thermal imaging, solar potential assessment of solar-powered vehicles, applications of drone-mounted hemispherical photography, and fisheye videos.

The effect of parameters and irradiance estimation techniques on PV potential quantification: The case of Mexico

The impact of some irradiance estimation techniques and model parameters in the assessment of solar potential has not yet been clarified, therefore, the case of Mexico is used in this work is to reduce such a gap. Firstly, a universe of 1629 weather stations and 108 models were used to study the effect of model-based and interpolation techniques on the estimation of the resource potential. Secondly, a centralized and two decentralized geographic potential scenarios were analyzed. Thirdly, the effect of parameters related to PV solar harvesting and production costs was evaluated. Our results revealed dramatic differences in the resource potential when heuristics models were used; differences up to 10% were found for the same location; in contrast, the evaluated interpolation techniques provided very close results with sensibility indexes (SI) below of 2.11%. Differences up to 15% were found in the average daily irradiance when the available area for PV installation is considered, which revealed the importance of the determination of suitable areas for harvesting. Changes up to %27 in the technical potential were found for different c Si-Poly modules. The parameters affecting most significantly the cost were the annuity factor (SI = 75.89%), the balance of the system (SI = 56.95%) and the PV module investment cost (39.28%). Our results provide reasonable results when compared with actual prices and provide evidence of the role of government policies to lower the costs.

Solar potential assessment over canal-top using geospatial techniques

This study has been carried out to assess the solar potential over canal-top. Remote sensing, geographic information system (GIS), and cloud environment have been applied to assess the solar potential available over canal-top in Roorkee Tehsil, India. Satellite image from Google Earth has been downloaded to extract the usable area for solar photovoltaic (PV) over Ganga canal-top. A geodatabase has been created using QGIS, by digitizing the canal within Roorkee Tehsil. Global horizontal irradiance (GHI) has been taken from a pyranometer and website developed and maintained by the National Renewable Energy Laboratory (NREL), United States, in collaboration with the Ministry of New and Renewable Energy (MNRE), India, for calculations. These GHI values have been converted into a tilted GHI to get an accurate solar potential assessment. A Perl script has been developed to convert the GHI into the tilt GHI. A solar potential assessment of the Salhapur village has been performed. Buffer analysis over canal has been performed to estimate the number of villages and towns for solar potential assessment. Buffer analysis over canal shows that 83% of towns and 46% of villages can be benefitted with the canal-top solar energy in Roorkee. The average solar potential assessed over Ganga canal-top for 15 years, i.e., from 2000 to 2014 is 12.746 GWh/year. The instantaneous value (18 September 2018) of solar potential at canal-top using pyranometer data is 1.85 GW. It is sufficient to fulfill the requirements of farmers, villages, towns, and educational institutes at Roorkee.

Solar potential assessment of public bus routes for solar buses

Current work investigates a method for evaluating the solar potential of public bus routes for solar electric buses. As access of solar radiation to roads is generally hindered by natural and man-made structures in the surroundings, the methodology involved taking several fisheye images along the chosen bus route. The visible sky and the structures were then separated using an automated image processing algorithm, also presented in this work. Then, these processed images were deployed in a solar assessment model to yield the weighted average yearly solar irradiation on the route. This was then compared with the energy requirements of an electric bus to see what fraction of its demand could be offset. For the case study, an existing public bus route in Invercargill (New Zealand) was analyzed. An appropriate fisheye camera was installed on the rooftop of a vehicle to obtain the images. It was found that installing solar panels on the rooftop of an electric bus may offset ∼8.5% of the electricity demand. This study will help councils (and/or bus contractors) to make decisions about moving to solar photovoltaic integrated electric buses, based on their designated routes.

Solar Potential Assessment using PVsyst Software in the Northern Zone of Morocco

The growing demand for solar photovoltaic (PV) installation systems as a renewable energy source is required in terms of accuracy to predict system efficiency. We have adopted in this paper a complete demonstration of the modelling and simulation of a 1 MW photovoltaic plant in one zone located in the north of Morocco, as a study; we will therefore use the PVsyst software for this purpose and evaluate its performance. The performance ratio and power losses such as temperature and electric power are calculated. As a result, the possibility of installing a 1 MW solar plant is examined by comparing the solar energy produced at certain sites in the northern zone of Morocco.

3D Solar Potential in the Urban Environment: A Case Study in Lisbon

The assessment of solar potential in the urban environment is an important instrument for policy decision regarding renewable energy deployment in the city. This paper presents an experimentally validated 3D solar potential model for rooftops and facades from LIDAR data considering anisotropic diffuse irradiation. The data visualization is rendered in the ArcGIS platform using CityEngine to automatically generate 3D models from 2D geometries. The model is validated against summer and winter measurements of photovoltaic performance on a facade. A case study for two densely packed urban areas in Lisbon, Portugal, are presented. Facades are shown to increase the solar potential by 10 to 15%.

USING GIS AND PHOTOGRAMMETRY FOR ASSESSING SOLAR PHOTOVOLTAIC POTENTIAL ON FLAT ROOFS IN URBAN AREA CASE OF THE CITY OF BEN GUERIR / MOROCCO

Abstract. Renewable energy sources are at the forefront of political discussions around the world because of the scarcity of fossil fuels and climate change caused by the accumulation of greenhouse gases. By 2030, Morocco will cover 52% of these energy needs through renewable energies, in order to preserve the environment (COP 22). This paper aims to estimate the potential of photovoltaic solar energy from flat roofs in the city of Ben Guerir, Morocco using remote sensing and GIS data. To achieve this goal, vector orthophoto resulting from the photogrammetric restitution acquired in 2015 were used to generate a 3D model (DSM). The annual solar irradiation is calculated by the analyser of the solar tool. Each roof is calculated based on algorithms for the most common solar panel technologies (mono-si and poly-si). The applicability of this methodology has been demonstrated in the urban area of Benguerir, Morocco, and can be widespread in any other region of the world. The results obtained for a total roofing surface of 135 Ha, i.e. more than 345 Gwh of electricity annually generate. For an average roof of 60 m2 that could supply 5 to 6 households; A planned investment between 118,218 and 167,296 DH, and an annual maintenance charge of 2%. This study may be an initial assessment of solar potential in the city, which can be used to support the management decision regarding investment in the urban solar system.

New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks

Assessment of solar potential over a location of interest is introduced as an important step for the successful planning of solar energy systems (photovoltaic or thermal). The aim of this paper is to develop an automatic selection of ANN inputs from available parameters set for the estimation of horizontal daily global solar irradiation in the city of Fez (Morocco). This selection is carried out using the evolutionary artificial neural networks (EANN) with different genetic operators including selection, crossover and mutation taking into account the coding of ANN as an individual in the pool of evolution. The study includes also an exploration of the effect of maximum generations, population size, cutting points positions/nature and the mutation percentage on the performance. In order to validate the efficiency of the proposed EANN model, comparative study is established using an implemented heuristic search and forward stepwise regression models. To compare the performances in term of estimation of the proposed model, three temperature-based and K Nearest Neighbors (K-NN) models were implemented and tested in our case study. Results show that the proposed model, not only achieves good solar irradiation estimation performance but also allows an automatic selection of the best input parameters for the ANN with lower population size and maximum generation ensuring good computation time.

3D model discretization in assessing urban solar potential: the effect of grid spacing on predicted solar irradiation

The increasing interest in solar energy production in urban areas requires an accurate simulation of solar irradiation on building surfaces, including vertical surfaces. However, solar potential analyses are usually conducted on 2.5D models, which are limited to roof surfaces. Methods based on 3D models, instead, allow the simulation of solar radiation on all building surfaces also accounting for inter-reflections. 3D models are thus discretized by grids of sensor points on which the solar potential is calculated.This paper investigates the discretization error in the assessment of solar potential based on 3D models. To this end, we tested the sensitivity of simulated solar irradiation to the resolution of the sensor grid. We analyzed the impact of the grid resolution using typical discretization approaches affecting the spatial arrangement of the sensor points.The study was conducted in a dense area of the city of Geneva represented at level of detail (LOD) 2. The simulated solar irradiation on 109 buildings was analyzed at different spatial, i.e. per surface and per building, and temporal granularities, i.e. hourly and yearly.The results show that the error increases linearly for grids spaced at up to 4 m with maximum relative root mean square error lower than 7%. The impact of the grid resolution was found greater for structured grids than unstructured grids. The results also highlight that finer grid resolutions (i.e. smaller spacing) are necessary if the analysis is conducted at high spatial or temporal granularity, notably when analyzing roof surfaces with shading artifacts.

ELECTRICITY INFRASTRUCTURE PLANNING: EVALUATING SOLAR POTENTIAL ASSESSMENTS FOR INFORMAL SETTLEMENTS USING GRASS AND FREELY AVAILABLE DATA

Abstract. Informal settlements, also known as slums or shanty towns, are characterised by rapid and unstructured expansion, poorly constructed buildings, and in some cases, they are on disputed land. Such settlements often lack basic services, such as electricity. As a result, informal settlement dwellers turn to hazardous alternative sources of energy, such as illegal electricity connections and paraffin. Solar power is a clean and safe alternative. However, informal settlements are often located on undesirable land on the urban fringe where the topography may hinder the use of solar energy. The high density of dwellings could also be a hindrance. Therefore, the solar potential needs to be assessed before any implementations are planned. Solar potential assessment functionality is generally available in geographic information system (GIS) products. The nature, cost and accessibility of datasets required for the assessment vary significantly. In this paper, we evaluate the results of solar potential assessments using GRASS (Geographic Resources Analysis Support System) for a number of different datasets. The assessments were done for two informal settlements in the City of Tshwane (South Africa): Alaska, which is nestled on a hill; and Phomolong, a densely populated settlement with a rather flat topography. The results show that solar potential assessments with open source GIS software and freely available data are feasible. This eliminates the need for lengthy and bureaucratic procurement processes and reduces the financial costs of assessing solar potential for informal settlements.

Comparison of artificial intelligence methods in estimation of daily global solar radiation

Abstract Assessment of solar potential over a location of interest is introduced as an important step for the successful planning of solar energy systems (photovoltaic or thermal). Due to the absence of meteorological stations and sophisticated solar sensors, solar data may be unavailable for every point of interest. Hence, empirical and intelligence methods are developed to estimate solar irradiance data. In this study, the idea of artificial intelligence methods is employed to predict the daily global solar radiation. The developed models are: group method of data handling (GMDH) type neural network, multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized with particle swarm optimization algorithm (ANFIS-PSO), ANFIS optimized with genetic algorithm (ANFIS-GA) and ANFIS optimized with ant colony (ANFIS-ACO). The data are collected from 12 stations in different climate zones of Iran. The input variables of the models are including month, day, average air temperature, maximum air temperature, minimum air temperature, air pressure, relative humidity, wind speed, top of atmosphere insolation, latitude and longitude. The results demonstrated that although the developed models can successfully predict the global horizontal irradiance, the GMDH model outperforms the other developed models. The values of root mean square error (RMSE), determination coefficient (R2) and mean square error (MSE) for the GMDH model were 0.2466 (kWh/m2/day), 0.9886 and 0.0608 (kWh/m2/day), respectively.

Cloud computing for energy requirement and solar potential assessment

The objective of this research is to derive an approach for the assessment of solar potential using cloud computing for a better energy planning. This approach is used to calculate energy requirement and solar potential having precise prediction probability. The energy requirement has been calculated based on the inputs such as the number of fans, tube lights, and electric pump with their wattage and usage hours. The assessment of the solar potential is based on the input parameters such as Global Horizontal Irradiance (GHI), sunshine hours, India Meteorological Department (IMD) data, cloud cover, tilted irradiance, etc. It is executed by software developed in Eclipse IDE (Integrated Development Environment), an open-source toolkit. Perl script has been used to convert the GHI onto the tilted surface which efficiently quantifies the collected solar irradiance. The IMD data are used for predicting the number of cloudy and rainy days for further estimation of solar potential. Cloud computing is used in uploading of the software module on the cloud. Google App Engine is used to deploy project information on the cloud. It has been found that enough solar potential is available to install Solar Photovoltaic (SPV) modules at Meerpur, India using software tool developed.

Global solar radiation on tilted surfaces in Tunisia: Measurement, estimation and gained energy assessments

A very important factor in the assessment of solar potential for the installation of photovoltaic plants is the availability of global irradiation data measurements. Such data must be collected over a period of time longer than 11 years and must be accurate. In some countries, it is difficult to have databases of these measures. To overcome this problem, we propose, the use of numerical models to estimate the monthly, seasonally and annually solar energy irradiation (global diffuse and direct solar radiation), especially on tilted surface.The results obtained from the numerical models are compared to the data collected from three regions on Tunisia: Bizerte (in the north), Nabeul (near to the north east) and Djerba (in the south).The actual measurements taken from the meteorological stations and the measurements generated by the numerical models are very close.After the validation of the numerical models, we tried to calculate the best tilt angle for each period of the year to position a photovoltaic panel, in a given region, to reach maximum energy recovery.The practical validation, of the optimal tilt angle search and the adequate period, was conducted at the Research and Technology Center of Energy of Borj Cedria. The obtained results are satisfactory and prove the reliability of the constructed numerical models.