Estimation Of Solar Potential Research Articles

Towards Sustainable Urban Energy: A Robust Deep Learning Framework for Solar Potential Estimation

Abstract. Rooftop photovoltaic is considered as a cost-effective and environmentally friendly solution to energy challenges in urban areas. To ensure photovoltaic efficiency, it is essential to accurately estimate rooftop solar potential and deploy solar panels wisely. During the past few years, deep learning-based estimation methods have emerged and mainly rely on inferring rooftop orientations from aerial imagery. However, we note that rooftops often appear diversely when images are taken at different solar azimuths, and this can lead to orientation misclassification. To address this, we propose a robust solar potential estimation framework, mainly composed of a rooftop orientation prediction network and a bilateral solar potential estimation module. Specifically, we first classify rooftops into five orientations, i.e., east, west, south, north towards, and flat with a semantic segmentation network. Afterward, opposing orientations are merged to alleviate misclassification caused by variant data acquisition time. Eventually, we compute solar potentials based on PVGIS and a weighting scheme. Experimental results on the RID dataset demonstrate the effectiveness of our approach in improving the accuracy of solar energy estimation.

Novel GPU-accelerated high-resolution solar potential estimation in urban areas by using a modified diffuse irradiance model

In the past years various methods have been developed to estimate high-resolution solar potential in urban areas, by simulating solar irradiance over surface models that originate from remote sensing data. In general, this requires discretisation of solar irradiance models that estimate direct, reflective, and diffuse irradiances. The latter is most accurately estimated by an anisotropic model, where the hemispherical sky dome from arbitrary surface’s viewpoint consists of the horizon, the circumsolar and sky regions. Such model can be modified to incorporate the effects of shadowing from obstruction with a view factor for each sky region. However, state-of-the-art using such models for estimating solar potential in urban areas, only considers the sky view factor, and not circumsolar view factor, due to high computational load. In this paper, a novel parallelisation of solar potential estimation is proposed by using General Purpose computing on Graphics Processing Units (GPGPU). Modified anisotropic Perez model is used by considering diffuse shadowing with all three sky view factors. Moreover, we provide validation based on sensitivity analysis of the method’s accuracy with independent meteorological measurements, by changing circumsolar sky region’s half-angle and resolution of the hemispherical sky dome. Finally, the presented method using GPPGU was compared to multithreaded Central Processing Unit (CPU) approach, where on average a 70x computational speedup was achieved. Finally, the proposed method was applied over a ∼21km2 urban area, obtained from Light Detection And Ranging (LiDAR) data, where the computation of solar potential was performed in a reasonable time.

Solar cadastre for assessment of near-zero energy districts

The building sector is responsible for approximately 40% of the total energy consumption in Europe, with most of it allocated to space heating. Additionally, a continuous increase has been observed in conventional energy prices, as well as a dependency of energy supply on geopolitical stability. Building energy independence and net zero energy buildings and districts are presented as a way to overcome these issues, with the addition of on-site renewable energy, and specifically solar energy, exploitation. The intention to utilise solar energy on-site creates the need for solar mapping in urban areas. A dynamic solar potential estimation is essential for the proper dimensioning of solar systems, and it is conducted in this study via the Energy3D software. A typical urban neighbourhood in Thessaloniki Greece is selected and a simplified version of a Solar Cadastre for the area is produced. The annual solar potential of the selected buildings is calculated and analysed, and the parameters affecting it are presented. Additionally, an estimation regarding the electricity production of typical photovoltaic panels installed on the building surfaces is conducted. The calculations highlight the importance of vertical facades as building integrated photovoltaic (BIPV) systems carriers.

SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery

SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery

Feasibility assessment of solar photovoltaic deployments on building surfaces with the constraint of visual impacts

As a major component of social acceptance, visual impact is often considered a significant constraint in solar applications. Visual impact assessment of solar applications, however, has been limited to pedestrians in previous studies. The extent to which PV systems can have visual impacts on occupants and whether it is necessary to include occupants in the measurement of visual impact remains uncertain. To fill this gap, we extended it from pedestrians to occupants and proposed a quantitative method to integrate pedestrians and occupants into a framework, combining the estimation of solar potential for the feasibility assessment of PV applications in a built environment. The concept is tested with a real case, located in Qingdao city, China, to present the technical flowchart for the feasibility assessment of solar PV deployments with the visual constraint. Building surfaces with qualified solar irradiation and low visibility were identified and compared in two cases, that is, with and without the inclusion of occupants as the visual constraint. The comparison results show that the change of suitable building surfaces for solar applications is 172,306 m2 (21% of suitable area) and 126 GWh (19% of yield energy) across the study area, indicating the significance of including occupants in the visibility assessment for the deployment of solar applications. The proposed method considers the visual constraint for the feasibility assessment of solar applications from the perspective of pedestrians and occupants, and it is helpful to identify the suitable surfaces for the large-scale deployment of solar applications at an early planning stage of solar city.

Impacts of surface model generation approaches on raytracing-based solar potential estimation in urban areas

Raytracing-based methods are widely used for quantifying irradiation on building surfaces. Urban 3D surface models are necessary input for raytracing simulations, which can be generated from open-source point cloud data with the help of surface reconstruction algorithms. In research and engineering practice, various algorithms are being used for this purpose; each leading to different mesh topologies and corresponding performance. This paper compares the impacts of four different reconstruction algorithms by investigating their performance using DAYSIM raytracing simulations. The analysis is carried out for five configurations with various urban morphologies. Results show that the reconstructed models consistently underestimate the shading influence due to geometrical shrinkages that emerge from the various model generation procedures. The explicit algorithms, with Generic Delaunay a notable example, have better performance with less embedded error than the implicit algorithms in both daily and annual simulations. Results also show that diffuse irradiance is responsible for larger contributions to the overall error than direct components. This effect becomes more prominent when modeling reflected irradiation in urban environments. Additionally, the work shows that solar elevation and shading geometry types also affect the error magnitude. The paper concludes by providing reconstruction algorithm selection criteria for photovoltaic practitioners and urban energy planners.

An economically feasible optimization of photovoltaic provision using real electricity demand: A case study in New York city

Solar farming has been experiencing explosive development in recent years. However, developing solar farming in urban areas is challenged by the heterogeneous distribution of solar irradiation in spatial and degradation of photovoltaic (PV) efficiency that make the economic performance uncertain. To tackle this problem, this study develops a spatio-temporal analytic model and a techno-economic assessment model to optimize PV provision to ensure that a PV system can meet the electricity demand and obtain reasonable profit simultaneously. Specifically, based on the estimation of solar potential on three-dimensional urban envelopes, the study determines PV favorable locations that are quantitatively large and spatially concentrated. Then, PV capacities in two comparative architectures, i.e., self-reliance relying on own building surfaces and external-support seeking supports from external rooftops, are planned to meet real electricity demand. Furthermore, the PV capacity is optimized, constrained by a constant electricity rate without Feed-in Tariff, a decreasing PV efficiency, and an increasing cost for maintenance. A case study in New York City suggests that the optimized PV installation can significantly offset household electricity consumption. In addition, the estimated net profit is significant even in rigorous conditions, which is inspiring for promoting distributed solar harvesting and competing with the local electricity market. • Optimized PV capacity to meet real electricity demand and obtain net profit. • Developed spatio-temporal data analysis and spatial-aware techno-economic assessment. • The estimated net profit in New York City is significant even in strict conditions. • Inspiring for applying the method to promote distributed PV systems in global cities.

Solar Energy in the Nordic Built Environment: Challenges, Opportunities and Barriers

Within the framework Solar Heating and Cooling Programme of the International Energy Agency Task 51 “Solar Energy in Urban Planning”, case studies from Norway, Sweden, and Denmark were collected and analyzed through a comparative approach. The cases were first classified based on their urban characterization (existing and new urban areas) and then compared within the same country or in a cross-country perspective according to three areas of interest (i.e., Scale and planning process, Legislation and planning process, Targets and goals). The comparisons follow a common template of five sections describing the role of the involved stakeholders and highlighting challenges, barriers, and opportunities for the deployment of active solar systems and passive solar strategies. Both technical and non-technical aspects are considered. Among the technical aspects, the focus is on the adoption of solar energy strategies (e.g., solar accessibility, daylighting), the estimation of solar potential and energy generation. Regarding the non-technical aspects, the focus is on identifying barriers and challenges for the adoption of solar systems in relation to national and local legislation. The findings show that municipalities can have a crucial role in facilitating the adoption of solar energy solutions in cities by embracing ambitious visions and storytelling, as well as being directly financially involved as owners or subsidizing bodies. The findings also demonstrate the value of the use of indicators to evaluate the performance of masterplans, the combination of analogue and digital tools in the design process, and the performance of solar simulations from early stages to foster awareness among the involved stakeholders. Despite these positives, the Scandinavian legislation on solar energy utilization in the urban context still displays fragilities, making the creation of guidelines a pressing need.

Method for Estimating Solar Energy Potential Based on Photogrammetry from Unmanned Aerial Vehicles

This study presents a method to estimate the solar energy potential based on 3D data taken from unmanned aerial devices. The solar energy potential on the roof of a building was estimated before the placement of solar panels using photogrammetric data analyzed in a geographic information system, and the predictions were compared with the data recorded after installation. The areas of the roofs were chosen using digital surface models and the hemispherical viewshed algorithm, considering how the solar radiation on the roof surface would be affected by the orientation of the surface with respect to the sun, the shade of trees, surrounding objects, topography, and the atmospheric conditions. The results show that the efficiency percentages of the panels and the data modeled by the proposed method from surface models are very similar to the theoretical efficiency of the panels. Radiation potential can be estimated from photogrammetric data and a 3D model in great detail and at low cost. This method allows the estimation of solar potential as well as the optimization of the location and orientation of solar panels.

GPU-Enabled Shadow Casting for Solar Potential Estimation in Large Urban Areas. Application to the Solar Cadaster of Greater Geneva

In the context of encouraging the development of renewable energy, this paper deals with the description of a software solution for mapping out solar potential in a large scale and in high resolution. We leverage the performance provided by Graphics Processing Units (GPUs) to accelerate shadow casting procedures (used both for direct sunlight exposure and the sky view factor), as well as use off-the-shelf components to compute an average weather pattern for a given area. Application of the approach is presented in the context of the solar cadaster of Greater Geneva (2000 km2). The results show that doing the analysis on a square tile of 3.4 km at a resolution of 0.5 m takes up to two hours, which is better than what we were achieving with the previous work. This shows that GPU-based calculations are highly competitive in the field of solar potential modeling.

A novel computational methodology to estimate solar energy on building rooftops

AbstractPrecise estimation of solar radiation is essential for sustainable development and renewable energy consumption. Building rooftops are appropriate locations for solar energy devices. Traditional solar potential estimation approaches usually adopt technologies such as light detection and ranging, which are expensive and available only for existing sites. In this paper, a novel methodology is presented to estimate the solar radiation components for any building rooftop surrounded by obstacles, using the ray‐triangle‐intersection method and discretization of 3D sky vault into sky elements. The proposed methodology accepts a 3D model, including all significant obstacles that produce a triangulated surface mesh and a discrete map. This mesh and map are utilized with solar angles and radiation data to estimate direct and diffuse radiation on the surfaces of the 3D structures. To validate the proposed algorithm, we discuss two case studies. Finally, the 3D radiation maps are generated using solar irradiation data for Karachi, illustrating the effects of nearby obstacles. Several optimizations are applied to the developed algorithm for reducing computational cost. The significant advantage of the methodology is that the skymap is location‐independent. A skymap generated once can be used for the same 3D model located anywhere in the world, with the appropriate solar angles data. This work will benefit consultants, governments, and households in the efficient use of solar energy potential for both existing rooftops and future constructions.

Efficient solar radiation estimation using cohesive artificial neural network technique with optimal synaptic weights

Estimation of solar potential is vital for renewable energy applications. In several studies, artificial neural networks have been employed to model solar radiation using various meteorological parameters. The collection and availability of the most appropriate input parameters is important for getting an accurate artificial neural network model. The present study aims to estimate the global solar radiation using different meteorological parameters and identify the significant parameters based on the analysis of synaptic weights in an artificial neural network model using the connection weight approach. Initially, artificial neural network and empirical models is applied to estimate the solar radiation in Chamba region. The artificial neural network architecture 5-48-15-1 resulted in minimum mean absolute percentage error of 12.15%. The mean absolute percentage error values for linear models are found to be 18.95%, 15.39%, and 21.62%, respectively. Thereafter, connection weight approach is applied to find significant parameters. The efficacy of the approach has been shown through a case study related to estimation of solar radiations in the Hamirpur region situated in the state of Himachal Pradesh (India). Five input parameters, namely temperature (T), relative humidity (RH), clearness index (KT), precipitation (PT), and pressure (P), have been considered to estimate solar radiations using a feed-forward neural network. The proposed approach infers that temperature is the most significant parameter followed by humidity and pressure. The clearness index and precipitation has been found to have the least effect on the estimation of solar radiations. Results also indicate that artificial neural network based technique is more accurate compared to empirical model.

Per-point processing for detailed urban solar estimation with aerial laser scanning and distributed computing

This paper presents a complete data processing pipeline for improved urban solar potential estimation by applying solar irradiation estimation directly to individual aerial laser scanning (ALS) points in a distributed computing environment. Solar potential is often measured by solar irradiation – the amount of the Sun’s radiant energy received at the Earth’s surface over a period of time. To overcome previous limits of solar radiation estimations based on either two-and-a-half-dimensional raster models or overly simplistic, manually-generated, geometric models, an alternative approach is proposed using dense, urban aerial laser scanning data to enable the incorporation of the true, complex, and heterogeneous elements common in most urban areas. The approach introduces a direct, per-point analysis to fully exploit all details provided by the input point cloud data. To address the resulting computational demands required by the thousands of calculations needed per point for a full-year analysis, a distributed data processing strategy is employed that introduces an atypical data partition strategy. The scalability and performance of the approach are demonstrated on a 1.4-billion-point dataset covering more than 2 km2 of Dublin, Ireland. The reliability and realism of the simulation results are rigorously confirmed with (1) an aerial image collected concurrently with the laser scanning, (2) a terrestrial image acquired from an online source, and (3) a four-day, direct solar radiation collection experiment.

Estimation of Direct Normal Irradiance at Antarctica for Concentrated Solar Technology

The estimation of the average daily, monthly and annual direct normal solar irradiation(DNI) was done in the region hosting the Mario Zucchelli Station, in the bay of Terra Nova(Antarctica). Estimates are based on measurements of direct normal (DNI), horizontal global (GHI)and horizontal diffuse (Diff.HI) irradiations, observed by a solar-metric acquisition station installedduring the XXVIII scientific expedition carried out in the austral summer 2012/2013 as part of theNational Plan of Research in Antarctica (PNRA). The contemporary observations of all threeirradiations allowed the setting up of the model for the estimation of the DNI starting from thevalues of the GHI only, validated for the weather conditions of the Antarctic region. Subsequently,the long-time data reconstruction of the DNI values thanks to the availability of several years' hourlymeasurements of GHI at the Mario Zucchelli base has been carried out using the meteorologicalacquisition data installed both at the base and in places scattered around it. The final results makefeasible the estimation of solar potential for concentrated solar technology according to longmeasurements of GHI. Overall, we propose a clean technology based on a renewable power plantand a specific example with a tendency toward a decreased human carbon footprint in theatmosphere of this protected area.

Characterization and modeling of solar radiation on the ground, application to the estimate of solar potential available on the coast of Nouakchott

The purpose of this work is to evaluate four empirical models developed in literature such as Ghouard, Perrin Brichambaut, Bird and Hulstrom and Dependent model of the Link trouble factor. The validation of these different theoretical models was carried out using solar global radiation data collected on the Cheikh Zayed Nouakchott power plant. These measurements were carried out at a time step of 5 minutes during the period of one year. (April 1, 2015 to March 31, 2016). The results show that the Ghouard model presents notable performances compared to the other models, with a determination coefficient of 96.2%. This validation also shows that the solar radiation measured and that estimated by the different models have the same pace and have good performance with a coefficient of determination greater than 93%. The available solar potential estimation was carried out using the actual measurements collected on the Cheikh Zayed plant. The results obtained showed that the available solar potential is 5 kWh / m² / d.

Utilities of Virtual 3D City Models Based on CityGML: Various Use Cases

Virtual 3D city models are increasingly being used to model the realms of the real world for utilization in a number of applications related to environmental simulations including, urban planning, mapping the energy characteristics of buildings, noise mapping, flood modelling, etc. Apart from geometric and appearance/textural information, these applications have a requirement for complex urban semantics. Currently, a number of 3D standards are available in CAD, BIM and GIS related domains for the storage, visualization and transfer of 3D geospatial datasets. Initially, the 3D data models (such as COLLADA, VRML, X3D, etc.) were purely graphical/geometrical in nature and mainly used for visualization purposes. With the inclusion of thematic modules in OGC CityGML, the integration of geometry and semantics in a single data model paved the way for better sharing of virtual 3D city models. In spite of the availability of a wide range of 3D data standards, there are certain differences with respect to geometry, topology, semantics, LODs, etc., which complicates the integration of 3D geodata from heterogeneous sources. This paper serves to highlights the need for the innovative solutions with respect to the urban environmental related simulations primarily based on the use of virtual 3D city models. Four use cases are studied in this context namely, (1) urban solar potential estimation using CityGML models, (2) simulation of traffic noise level mapped on building walls from the urban road segments, (3) CityGML based 3D data models interoperability, and (4) 3D indoor logistics and subsurface utilities. However, for modelling majority of use cases, CityGML does not provide explicit thematic representations but provides support for extending the CityGML schema using Application Domain Extensions. In a nutshell, the study explores the semantic modelling capabilities of the CityGML for the transformation of native 3D virtual city models to one satisfying capabilities like semantic information and support towards interoperability.

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.

ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building

An accurate solar potential estimation of a specific location is basic for the solar systems evaluation. Generally, the global solar radiation is determined without considering its different contributes, but systems as those concentrating solar require an accurate direct normal irradiance (DNI) evaluation. Solar radiation variability and measurement stations non-availability for each location require accurate prediction models. In this paper two Artificial Neural Network (ANN) models are developed to predict daily global radiation (GR) and hourly direct normal irradiance (DNI). Two heterogeneous set of parameters as climatic, astronomic and radiometric variables are introduced and the data are obtained by databases and experimental measurements. For each ANN model a multi layer perceptron (MLP) is trained and validated investigating nine topological network configurations. The best ANN configurations for predicting GR and DNI are tested on different new dataset. MAPE, RMSE and R2 for the GR model are respectively equal to 4.57%, 160.3 Wh/m2 and 0.9918, while for the DNI they are equal to 5.57%, 17.7 W/m2 and 0.994. Hence, the proposed models show a good correlation both between measured and predicted data and with the literature. The main results obtained are the DNI and the GR models predicting which have allowed the evaluation of the electric energy production by means of two different photovoltaic systems used for a residential building. Hence, the developed ANN models represent a good tool to support the assessment of the green energy production evaluation.

GPU-based roofs' solar potential estimation using LiDAR data

Solar potential estimation using LiDAR data is an efficient approach for finding suitable roofs for photovoltaic systems' installations. As the amount of LiDAR data increases, the non-parallel methods take considerable time to accurately estimate the solar potential. Although supercomputing provides a possible solution, it is still too expensive and thus infeasible for general usage. Fortunately, the recent graphics processing units (GPUs) can now be utilized to ensure fast computations. This paper proposes a novel method for fast solar potential estimation using GPU-based CUDA technology. This method employs LiDAR data, irradiance measurements, multiresolutional shadowing from solid objects, and heuristic shadowing from vegetation. Experimental results demonstrate the method's effectiveness, in comparison with a multi-core CPU-based approach.

05/00240 Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data: Sözen, A. et al. Energy Conversion and Management, 2004, 45, (18–19), 3033–3052

Functions for total wheat production, the major agricultural crop of Gaziantep, Sanliurfa, Diyarbakir, and Mardin provinces in Southeastern Anatolian Project (SAP) region in Turkey were developed by using annual data for the 1963–1989 period. Regression analyses were carried out on the multiple-linear, quadratic, and Cobb-Douglas functional forms of total wheat production in the region effected by cultivation area, fertilizer usage, and rainfall. From the economical theories' point of view the model revealed good insight. The models were statistically significant at the 95% confidence level. Irrigation is valued quite substantially. Output elasticities of inputs are less than 1 and function coefficient is elastic. Thus input employment can be increased to increase wheat production. Further research based on factor analysis is recommended.