Solar Maps Research Articles

Using Partial Solar Eclipse for the 14-Metre Radio Telescope Calibration

Abstract A partial solar eclipse occurred on 25 October 2022, in the central and northern parts of Europe. The partial solar eclipse was observed at Aalto University Metsähovi Radio Observatory, Finland at the radio wavelength of 8 mm (37 GHz). In Finland, the magnitude of the partial solar eclipse was 62.7 %. Solar radio maps at the time cadence of about 9 minutes were observed over the whole eclipse. The solar eclipse observations could be used for instrument calibration purposes. This paper investigates the solar brightness temperature, the limb brightening effect, the height of the chromosphere and the location of radio brightening using the aforementioned partial solar eclipse observations. We got the confirmation that our earlier results are consistent, e.g., the solar brightness temperature matches with 8100 K ± 300 K. It was also possible to detect limb brightening effect. However, the prevailing solar activity might have distorted the final conclusions. The Moon should operate as a focusing element and the location of radio brightenings could be defined more carefully than in the normal conditions. We investigated this feature. Our results are in some parts unexpected and need further investigations.

Site suitability assessment for solar power plants in Bangladesh: A GIS-based analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) approach

Solar power plants are important alternatives to fossil fuel-based power plants because they reduce greenhouse gas emissions and mitigate the effects of climate change. The potential of harnessing solar energy is highly dependent on selecting the optimal locations for plant installation. This study primarily aims to select optimal sites for solar energy plant installation using the Analytic Hierarchy Process (AHP) of Multi-Criteria Decision Analysis (MCDA) in a Geographic Information System (GIS) environment. In the underlying processing phase, the AHP method was used to calculate the relative weights of the study area's physiographic, utility, and climate data. These weights were then used in the MCDA to identify the optimal locations for solar plants. The results of the MCDA were presented in the form of a solar plant suitability map, which showed that 44.59 % (66506.49 km2) of the study area in the south and southwest of Bangladesh is highly suitable for the development of utility-scale solar power plants. In contrast, there are substantial areas of about 31.18 % (46507.37 km2) of the country where the installation of any type of power plant is completely prohibited. This research can help authorities and stakeholders decide where to invest in solar energy.

Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration

Sibuyan Island is experiencing a significant increase in electricity demand due to population growth, urbanization, and industrial development. The island plans to use solar energy, recognizing its abundance and renewable nature; thus, this study was conducted to visualize the spatial distribution of solar exploration suitability using a geographic information system (GIS). Various criteria, including climatology, location, geography, meteorology, and disaster susceptibility, were considered in the assessment. Parameters affected by government policies, such as protected areas, proximity to rivers, roads and faults, ancestral domains, and proclaimed watersheds, were also considered. Parameters were weighted, and suitability levels were highlighted using AHP. The study revealed that about 5.88% (2674.06 km2) of the island was categorized as highly suitable for a solar farm, 34.99% (15,908.21 km2) as suitable, 2.49% (1129.95 km2) as moderately suitable, and the majority, 56.64% (25,754.47 km2), was considered not suitable for solar projects. A solar power suitability map was developed as a reference for local governments and residents in establishing solar PV systems in their respective sites, thus maximizing the full potential of their land. The study also directs future solar power exploration studies in Sibuyan Island, supporting ongoing efforts to maximize solar energy utilization.

An investigation of solar energy potential towards improving agriculture using angstrom and newly developed analytical models: In case of The Gambia

Cleaner and affordable energy demand in The Gambia has gained a lot of prominence in recent years, with many people shifting towards solar PV installation. However, the interest in utilizing solar energy has continued to witness a remarkable setback in developing countries due to economic factors and the limitations of in situ measurements. The latter has steered researchers to explore empirical models to predict global solar radiation with the aid of available meteorological data. This study estimates monthly global solar radiation (HG) using the modified Angstrom models and a newly developed model at two locations in The Gambia using secondary data collected from the Yundum and Basse meteorological stations for twenty years (2000–2019) to evaluate the prospects for the siting and implementing of solar projects. The results ranged from 9.0-27.0 MJm−2day−1 or 2.5–7.5 kWh/m2/day for Yundum and 8.0–28.0 MJm−2day−1 or 2.2–7.8 kWh/m2/day for Basse. The models’ results were also compared with the solar resource maps and GIS data for The Gambia locations and tested with the statistical tools of Root mean square error, Correlation coefficient, Normalized mean square error, and Mean percentage error to validate their performances and found to show a good agreement on monthly basis. However, the success of these models drastically declined when estimated on daily or hourly basis. The potential of utilizing the outcome in the agriculture sector of the economy was also x-rayed and justified. Even the lowest value of 8.0 MJm−2day−1which occurred in December has the potential of generating solar energy. This signifies that solar energy can be explored to make up for the energy shortfall in The Gambia's agriculture and other sectors of the economy. The modified Angstrom equations for Yundum and Basse and our new model used in estimating the HG and the regression parameters can be applied to estimate the HG for the subsequent years at the same locations. We recommend that similar studies be conducted at other locations using our newly developed model for comparison and generality of the result.

The use of budget UAV systems and GIS spatial analysis in cadastral and construction surveying for building planning

The uncertainty that comes with planning, constructing, and maintaining buildings is a constant issue for architects and civil engineers. As topography is the framework that unites architecture and landscape, the design and planning projects heavily rely on a range of monitoring, surveying methods and comprehensive field data. Along with the traditional topo-geodetic instrumentation used in land and construction surveying, unmanned aerial vehicles equipped with digital cameras and structure from motion software have been increasingly used recently in a variety of fields to create high-resolution digital elevation models. Despite this widespread use, in the majority of surveying projects it is considered that the topographic representations produced through this technology is inferior to that obtained with surveys conducted using conventional methods, along with other constraints imposed by legislation, environment and weather conditions. While certain limitations of unmanned aerial vehicle (UAV) systems are challenging, their advantage for gathering data from a different perspective and the generated outputs have the potential to significantly advance the construction industry. The present article provides an overview of the usefulness of budget UAV systems in developing a methodology that accompanies the conventional survey process for civil engineering applications. Thus, along with the established survey for cadastral and technical documentations necessary for the architectural process, a complementary UAV survey was developed, with subsequent spatial analysis in a geographic information system (GIS), in order to expand the array of deliverables. These include useful orthophoto map, larger-scale and denser representations of the topography, digital surface and terrain models, slope, aspect and solar radiation maps which will offer helpful information and instructions at the start of the construction planning process. The methodology contains two case studies with different degrees of terrain and vegetation challenges, and also presents an accuracy assessment and overall benefits discussion regarding the UAV implementation.

Real-Time Terrain Correction of Satellite Imagery-Based Solar Irradiance Maps Using Precomputed Data and Memory Optimization

Satellite imagery-based solar irradiance mapping studies are essential for large-scale solar energy assessments but are limited in spatial resolution and accuracy. Despite efforts to increase map resolution by correcting inaccuracies caused by shadows on the terrain, the computational time of these models and the massive volume of generated data still pose challenges. Particularly, forecasting generates large amounts of time series data, and the data production rate is faster than the computational speed of traditional terrain correction. Moreover, while previous research has been conducted to expedite computations, a novel and innovative technology in terrain correction is still required. Therefore, we propose a new correction method that can bypass complex calculations and process enormous data within seconds. This model extends the lookup table concept, optimizes the results of many shadow operations, and stores them in memory for use. The model enabled 90 m scale computations across Korea within seconds on a local desktop computer. Optimization was performed based on domain knowledge to reduce the required memory to a realistic level. A quantitative analysis of computation time was also conducted, revealing a previously overlooked computational bottleneck. In conclusion, the developed model enables real-time terrain correction and subsequent processing of massive amounts of data.

Improving Global Solar Magnetic Field Maps: Why Multiple Low- and High-latitude Vantage Points are Necessary

Improving Global Solar Magnetic Field Maps: Why Multiple Low- and High-latitude Vantage Points are Necessary

A regional solar forecasting approach using generative adversarial networks with solar irradiance maps

The intermittent and stochastic nature of solar resource hinders the integration of solar energy into modern power system. Solar forecasting has become an important tool for better photovoltaic (PV) power integration, effective market design, and reliable grid operation. Nevertheless, most existing solar forecasting methods are dedicated to improving forecasting accuracy at site-level (e.g. for individual PV power plants) regardless of the impacts caused by the accumulated penetration of distributed PV systems. To tackle with this issue, this article proposes a novel generative approach for regional solar forecasting considering an entire geographical region of a flexible spatial scale. Specifically, we create solar irradiance maps (SIMs) for solar forecasting for the first time by using spatial Kriging interpolation with satellite-derived solar irradiance data. The sequential SIMs provide a comprehensive view of how solar intensity varies over time and are further used as the inputs for a multi-scale generative adversarial network (GAN) to predict the next-step SIMs. The generated SIM frames can be further transformed into PV power output through a irradiance-to-power model. A case study is conducted in a 24 × 24 km area of Brisbane to validate the proposed method by predicting of both solar irradiance and the output of behind-the-meter (BTM) PV systems at unobserved locations. The approach demonstrates comparable accuracy in terms of solar irradiance forecasting and better predictions in PV power generation compared to the conventional forecasting models with a highest average forecasting skill of 10.93±2.35% for all BTM PV systems. Thus, it can be potentially used to assist solar energy assessment and power system control in a highly-penetrated region.

Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China

Numbers and sizes of photovoltaic solar power plants have grown unprecedentedly over the last few years in China, which aims to achieve a carbon emission peak by 2030 and carbon neutrality by 2060. Thus, timely and accurate monitoring of photovoltaic solar power plants is crucial to the design and management of renewable electricity systems in China. Random forest algorithm has been used to map photovoltaic solar power plants at multiple scales, however, it always causes several salt-and-pepper noises, limiting its application at larger spatial scales. Here we first develop a photovoltaic solar power plant mapping method through integrating time series Landsat imagery, random forest, and morphological characteristics. Then we apply this method in Gansu Province, which has abundant solar and wind energy resources and provide large amounts of potential lands for photovoltaic development, and generate the annual photovoltaic maps from 2015 to 2020. We further analyze the spatial-temporal dynamics of sizes and areas of photovoltaic solar power plants and major land cover conversion of expansive photovoltaic regions. Finally, we discuss the reliability, uncertainties, implications, and future development of our improved methods. We find our photovoltaic mapping method can remove most of salt-and-pepper noises effectively, and the resultant maps in Gansu for 2020 have very high accuracies with user's and producer's accuracies of 97.57% and 99.22%, respectively. There are 165.29 km2 photovoltaic solar power plants in Gansu for 2020, and most of which are located in the northwestern Gansu. In addition, the photovoltaic with patch size > 1 km2 and ≤ 2 km2 (53.4 km2, 32.3%) has largest patch number (39, 15.7%). The improved photovoltaic mapping methods and further analysis in this study provide critical information for accurate and automatic classification of photovoltaic solar power plants in the future, as well as the environmental and sustainable development of solar energy in China.

Solar Zoning Maps of Algeria Based on Sunshine Duration Data and Kriging Method

Solar Zoning Maps of Algeria Based on Sunshine Duration Data and Kriging Method

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.

Assessment of the solar energy potential of diverse urban built forms in Nagpur, India

Power consumption in urban areas is enormous and expected to rise exponentially in the coming years. Thermal power plants connected to the grid supply the majority of this electricity demand. Utilizing solar energy for photovoltaic (PV) panels may be a substitute for generating clean electricity. The heterogeneous urban built form (UBF) makes it a challenge for using solar energy effectively. Thus, it is crucial to understand how the UBF of cities affects solar energy potential physically, geographically and technically.This study assesses the solar energy potential of five diverse UBFs in Nagpur, India, identified using Local Climatic Zones (LCZ). A set of indicators under the urban form elements - buildings, density, open spaces, streets, and vegetations are used to assess the UBFs. Digitized 3-D models are prepared using ArcMap, ArcGIS Pro, and Rhinoceros 3D to perform solar simulation using Grasshopper + Ladybug plugin. Solar PV yield computation is done to understand the solar energy potential (SEP). The results show that each UBF affects the solar energy potential significantly. The methods utilized in this work can aid in the creation of urban solar maps. The study can be expanded for designing solar panels for retrofitting for specific urban setting.

Generating citywide street cross-sections using aerial LiDAR and detailed street plan

Precise information on spatial configurations of urban spaces, such as streets, is essential for investigating complex interrelationships between the quality of life, livability, and mobility. Street cross-sections can be central for such research as they depict the allocation of urban space by detailing the streetscape's layout and dimensions, including driveways, sidewalks, bikepaths, median strips, trees, adjoining buildings and open spaces, and shadows. However, creating an accurate description of the real world as a three-dimensional representation and translation into cross-sections is challenging due to the requirement of multiple data sources, such as road layouts with widths, height information, and, optionally, high-resolution aerial imagery. Without such datasets, the cross-section drawings are limited to manual measurements, which are difficult to extend to a city scale. This study aims to develop a method to automatically generate street cross-sections for the entire city of Berlin based on an aerial Lidar dataset and a city street plan. The study also includes shadows as a part of the sections and utilizes the Lidar dataset to generate solar radiation maps. Approximately 0.5 million cross-sections are generated with detailed information regarding the width and placement of the street elements. The details of the cross-sections are further processed to find generic street compositions, understand the influence of shadows on walkways and bikepaths, and calculate the enclosure.

Revealing a Shift in Solar Photovoltaic Planning Sites in Vietnam from 2019 to 2022

Solar photovoltaic (PV) technology has been widely used as a major source of renewable energy. Vietnam is especially active in installing solar energy systems. The total installed solar PV capacity in Vietnam has significantly increased since 2019, but the spatial evolution of solar panels is yet to be discussed. Therefore, this study aims to reveal the shift that occurred in solar photovoltaic planning sites in Vietnam from 2019 to 2022. Solar PV maps were produced from Sentinel-2 imagery via a deep learning segmentation model. Land cover maps, terrain slope, solar power potential, population density, and power grid datasets were compared to the locations of the detected PV sites each year to reveal a shift in the solar farm planning sites. The result show that the deep learning model achieved satisfactory performance. The observed shift in the PV installation sites suggests that for the first two years, large solar farms were built on suitable land near the electricity grid, while smaller PVs were constructed at locations less suitable for solar energy production in 2021 and 2022. These findings suggest that the shift in solar PV planning in Vietnam was caused by the availability of suitable land with an appropriate energy transfer capacity and the participation of smaller-scale PV operators.

An interactive web-based solar energy prediction system using machine learning techniques

Solar energy being one of the most inexpensive renewable energy sources, has shown to be a viable alternative to traditional fossil-fuel and wood-based electricity generation. For the purpose of creating a more trustworthy and successful energy planning strategy, accurate projections of sun irradiation, solar energy generation, and revenues are crucial. Hence, in this work we have proposed web-based optimal prediction system that estimates solar radiation based on location and meteorological data using Machine Learning techniques. Furthermore, an interactive dashboard solar digital map has been developed that enables real-time investigation of solar energy consumption, production, solar radiation, and investment potential for a specific county in California. The model's performance has been measured using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Average Error (MAE), and Mean Absolute Percentage Error (MAPE) scores. Experimental results demonstrate that stacking model outperformed all the models with the lowest RMSE, MSE, and MAE.

The Predicition of Solar Flares Using Millimeter Radio Brightenings

Abstract Solar activity could have significant impacts on various Earth and near-Earth space systems, such as satellite communication and power grid systems. The prediction of solar activity and active solar events plays a major role when preparing for these disturbances. Various satellite-based instruments constantly observe the Sun. However, only a few ground-based solar instruments could provide versatile enough information for the space weather prediction. Metsähovi Radio Observatory of Aalto University (Finland) has a unique collection of millimetre (8 mm) solar radio maps over the past 40 years, and even denser observational solar radio data catalogue since 2011. About 75–80 % days yearly are covered nowadays. This gives opportunity to make statistical estimation of solar flare occurrence based on solar radio maps. In this study, we had 2253 days when both solar radio map and GOES (Geostationary Operational Environmental Satellites) classified solar flare were observed. In this work, we used solar flare classification done by the Space Weather Centre (SWC) of the National Oceanic and Atmospheric Administration (NOAA). The data were observed between 1 January 2011 and 12 September 2022. Our study shows that the maximum intensity of radio brightenings is a good indicator to tell which kind of GOES classified solar flare could be expected to happen. The article presents that intense radio brightening is needed to produce a certain GOES classified solar flare.

The Solar Radiation Climate of Saudi Arabia

In the present work, we investigate the solar radiation climate of Saudi Arabia, using solar radiation data from 43 sites in the country covering the period 2013–2021. These data include hourly values of global, G, and diffuse, Gd, horizontal irradiances from which the direct, Gb, horizontal irradiance is estimated. The diffuse fraction, kd; the direct-beam fraction, kb; and the ratio ke = Gd/Gb, are used in the analysis. Solar maps of the annual mean G, Gd, kd, kb, and ke are prepared for Saudi Arabia under all- and clear-sky conditions, which show interesting but explainable patterns. Additionally, the intra-annual and seasonal variabilities of these parameters are presented, and regression equations are provided. We find that Gb has a negative linear relationship with kd; the same applies to G with respect to kd or the latitude, φ, of the site. It is shown that kd and kb can reflect the scattering and absorption effects of the atmosphere on solar radiation, respectively; therefore, they can be used as atmospheric scattering and absorption indices. Part of the analysis considers the defined solar energy zones in Saudi Arabia.

Acquisition and Processing Data from UAVs in the Process of Generating 3D Models for Solar Potential Analysis

UAVs have recently become a very popular tool for acquiring geospatial data. Photographs, films, images, and results of measurements of various sensors from them constitute source material for generating, among other things, photographic documentation, visualisation of places and objects, cartographic materials and 3D models. These models are not only material for the visualisation of objects but are also source material for spatial analysis, including the assessment and analyses of the solar potential of buildings. This research aims to benchmark the feasibility of using UAV-derived data acquired from three sensors, namely the DJI Zenmuse P1 camera, the Share PSDK102S v2 multi-lens camera and the DJI Zenmuse L1 laser scanner. The data from these were acquired for the construction of comprehensive and reliable 3D models, which will form the basis for generating solar potential maps. Various sensors, data storage formats, and geospatial data processing capabilities are analysed to determine the most optimal and efficient solution for providing accurate, complete and reliable 3D models of places and objects for the construction of solar potential maps. In this paper, the authors prepare a compilation of the results of the studies from different measurement combinations and analyse the strengths and weaknesses of the different solutions, as well as the integration of the results for an optimal 3D model, which was used to perform solar potential analyses for the selected built-up area. The results of the study show that the parameters for assessing the quality of a 3D model can be statistical parameters that determine the coplanarity of roof slope points (i.e., standard deviation, distances from the plane, and RMS value). The completeness of the model is defined as the percentage of the recorded area by sensors to the total area of the model.

Solar energy potential assessment for electricity generation on the south-eastern coast of Iran

Among the types of renewable energy, solar energy has received more attention due to its ability to convert directly into electricity and heat, its ease of use, its possibility of storage, and its endlessness, so in recent decades, a lot of research has been done on solar energy systems in the world and in Iran. Considering Iran's potential in the field of solar energy and the country's need for this type of energy, it is necessary to locate and identify suitable sites for the use of solar energy. In this research, the potential of generating power from solar energy on the ocean coasts of south-eastern Iran has been investigated. The geographical data of the solar radiation map of Iran was used to estimate the power of electrical energy from spatial limiting criteria for the feasibility of installing photovoltaic panels at the power plant scale. Finally, the total power of electricity that can be extracted from suitable places in the region was calculated; results showed that 37.5% of the Makran area is exploitable as solar farms. With a conversion efficiency of 15% and an area factor of 70%, annual electricity production for the exploitable area is roughly 17200 GWh, which can be a driving force for the industrial, economic and social development of Makran region.

Improvement of Korea Meteorological Administration Solar Energy Resources Map Using Fine-Scale Terrain Data

Real-time solar energy resources mapping is crucial for the development and management of solar power facilities. This study analyzes the effects of the digital elevation model (DEM) resolution on the accuracy of the surface insolation (insolation hereafter) calculated by the Korea Meteorological Administration solar energy mapping system, KMAP-Solar, using two DEMs of different resolutions, 1.5 km and 100 m. It is found that KMAP-Solar yields smaller land-mean insolation with the fine-scale DEM than the coarse-scale DEM. The fine-scale DEM reduces biases by as much as 32 Wm− 2 for all observation sites, especially those in complex terrain and that the insolation error reduction is correlated with the difference in sky view factor (SVF) between the coarse- and fine-scale DEM. Both the coarse- and fine-scale DEMs generate the insolation-elevation and insolation-SVF relationship which is characterized by positive (negative) correlation between the insolation and the terrain altitude (SVF). However, the coarse-scale DEM substantially underestimates these relationships compared to the fine-scale DEM, mainly because the coarse-scale DEM underrepresents large terrain slopes and/or small SVFs, most seriously in high-altitude regions. The fine-scale DEM generates a more realistic insolation distribution than the coarse-scale DEM by incorporating a wider range of key terrain parameters involved in determining insolation. Improvements of insolation calculations in KMAP-Solar using a fine-scale DEM, especially in the areas of complex terrain, is of a practical value for Korea because the operational solar resources map from KMAP-Solar supports solar energy research, solar power plant installations, and real-time prediction and management of solar power within the power grid.