Monthly Solar Radiation Research Articles

Modeling spatiotemporal distribution of yellow rust wheat pathogen using machine learning algorithms: Insights from environmental assessment

The yellow rust pathogen (Puccinia striiformis Westend) poses a significant threat to wheat production in the world, necessitating a comprehensive understanding of its spatiotemporal distribution and the influence of climatic factors. In this study, we employed an ensemble of four prominent machine learning algorithms to assess the impact of various environmental and remote sensing variables on the spread of yellow rust at a national scale. Our analysis incorporated 55 climatic parameters, including monthly temperature, precipitation, solar radiation, and wind speed. The results demonstrated that the RF algorithm yielded robust predictions, with a Receiver Operator Characteristic (ROC) of 0.916 and True Skill Statistic (TSS) of 0.748. Furthermore, the study identified key influencing variables for wheat disease modeling, such as annual precipitation, temperature seasonality, and isothermality. Projections based on the model indicate a potential decrease in disease spread by 2050 in specific regions. The findings underscore the efficacy of ensemble modeling in predicting the spatiotemporal distribution of yellow rust on a large scale, offering valuable insights for the development of robust agricultural management strategies in the face of evolving climate conditions.

Long-term solar radiation forecasting based on LSTM and attention mechanism: a case study in Algeria

The growing adoption of photovoltaic (PV) energy in urban areas underscores its capability to meet energy demands effectively. The accurate forecasting of meteorological parameters, particularly global solar radiation, is paramount for the efficient management and utilization of solar energy resources. Solar radiation forecasting methods typically fall into two main categories: cloud imagery combined with physical approaches, and Artificial Intelligence (AI) based methods. Due to the non-stationary nature of solar radiation and the high nonlinearity of atmospheric conditions, conventional forecasting approaches often exhibit poor accuracy. Artificial intelligence based approaches, such as machine learning and deep learning algorithms, are extensively employed in global solar radiation forecasting research, demonstrating impressive accuracy. In this respect, this paper presents a novel approach for long term monthly forecasting of global solar radiation using a Bidirectional Long Short-Term Memory (LSTM) architecture augmented with an attention mechanism that includes a Squeeze and Excitation (SE) block (SE-BiLSTM). The effectiveness of this model is extensively evaluated for long-term monthly solar radiation forecasting using meteorological data collected over a 20-year period (from Jan 2001 to Dec 2020) from the National Aeronautics and Space Administration (NASA). The proposed SE-BiLSTM model is compared with well-established forecasting models including Naïve, Autoregressive Moving Average (ARMA), Multi-layer Perceptron (MLP), LSTM, and Bidirectional LSTM. Through rigorous simulation tests, our model demonstrates superior performance, achieving the lowest mean absolute percentage error (MAPE) of 4.52% and mean absolute error (MAE) of 7.89 kW/m2. This advancement holds significant promise for enhancing solar energy forecasting accuracy and its practical application in renewable energy systems.

An economic feasibility assessment of implementing photovoltaic module reflectors under Malaysian meteorological conditions

The use of a reflector can increase the solar radiation on the photovoltaic module (PV) surface, whereby the energy output can be improved. However, the economic feasibility may need to be considered too. This study is conducted, for the first time, due to the lack of studies regarding the economic feasibility assessment of implementing reflectors under the Malaysian meteorological conditions. The outcome will give information about the suitability for implementing a PV reflector in Malaysia through an experimental setup at a sewage treatment site, for two months in 2022. The Malaysian meteorological data, which include daily solar radiation, ambient temperature and wind velocity, were collected to study the output energy, efficiency and the economic perspective of a PV. In February 2022, the PV was operating without a reflector and the averaged values for the monthly solar radiation, ambient temperature and wind velocity were 539.9 MJ/m2, 28.4 °C and 2.2 m/s, respectively, which resulted in an output energy of 106.43 kWh. On the other hand, for April 2022, the PV was operating with a reflector. With the respective averaged input parameters 544.98 MJ/m2, 28.9 °C and 1.51 m/s, the output energy was 121.94 kWh. It is thus shown that the PV with a reflector increases the PV’s output energy by 14.57%. Also, it is shown that the cost-effective factor value is 0.955 which means that the PV reflector is economically feasible to be implemented under the Malaysian meteorological conditions. Hence, extensive research should be conducted to improve the performance of PV reflectors. The findings of this paper maybe useful for researchers and/or manufacturers of PV reflectors.

Effect of adjusting orientation for solar energy applications in multiple climatic zones

South-facing collectors are the optimum choice for solar applications in the northern hemisphere. However, obstacles may limit the feasibility of this orientation. Therefore, altering the orientation of the collector impacts solar insolation. In this study, the Perez model is utilized to evaluate incoming solar radiation on tilted surfaces for solar collectors in four climatic zones across Pakistan. The results are presented in contour plots to analyze the optimal tilt and orientation for solar applications. The findings of the study indicate substantial energy gains when collectors are placed at optimum angles. More specifically, Quetta leads with a 14.54% increase, followed by Karachi and Multan at 9.81% and 9.3%, respectively, compared to horizontally placed collectors. Analysis of vertical surfaces reveals a notable decrease in monthly solar radiation, especially in Peshawar (37.22%). Monthly adjustments in tilt angles outperform fixed positions, enhancing solar energy intensity. When comparing yearly adjustments with monthly adjustments, Quetta shows the maximum increase of 5.92%, followed by Karachi (4.86%), Multan (4.01%), and Peshawar (3.65%). It is also observed that ±15° azimuth angle change from the south ensures receiving up to 98% of insolation, regardless of the climatic region. Lastly, the validation against the NASA SSE database further highlights the reliability of our simulation model. Overall, the outcomes of the study will contribute to informed solar energy planning in the studied regions.

Levelised hydrogen production costs for a standalone photovoltaic powered system in Australia

This article presents an analysis of levelised costs of producing hydrogen via the photovoltaic electrolysis in Australia. Eight locations (Darwin, Alice Springs, Brisbane, Perth, Sydney, Mildura, Melbourne, and Hobart) covering six climate zones in Australia are selected for the analysis. They receive varying solar radiation to produce electricity by using photovoltaic systems. In the present work, a standalone photovoltaic electrolysis and hydrogen engine system for 69 kWhe of daily electricity used is studied. Photovoltaic system is designed based on the minimum average monthly solar irradiation received for each selected location. The required photovoltaic array sizes are determined at the optimal slopes of the solar panels. By using the electricity generated from the photovoltaic, hydrogen is generated in electrolyser. The levelised cost of hydrogen was determined and found to be AU$ 6.79-10.62 kg-1. The electrolyser cost was found to be 52-55% of the life cycle cost of the system. Therefore, the cost of an electrolyser is the determinant factor for the levelised hydrogen production cost.

A novel approach for sizing and optimization of hybrid solar-PV, biogas-generator, and batteries system for rural electrification: case study

A new approach for sizing a hybrid solar-PV-battery and biogas generator for power generation was suggested in this study, based on the variation of energy resources and the load profile. Biogas has enormous potential and numerous economic and social advantages. In this respect, the monthly solar radiation, temperature, and biogas produced from biomass resources were used as model inputs. This study considers the total annualized of the components for the feasibility analysis. HOMER Pro® software-based outcomes in the proposed methodology revealed that the proposed solar biogas hybrid system was sufficient to meet the load requirements of the village (Ain Farba village in the Hodh El Gharbi area located in the northeast of the Mauritania country) with a net present cost over the 25-year lifespan of 61,144 $ and the energy cost was determined to be 0.0473 $/kWh.

Spatial characterization of climatic variables for Arica-Parinacota and Tarapacá, Chile using topoclimatic analysis

In the present study, models were developed to determine the monthly and annual spatio-temporal variation of temperature, precipitation, and solar radiation based on topoclimatic analysis of Arica-Parinacota and Tarapacá in northern Chile. To construct the equations of the topoclimatic model, the data from meteorological stations and physiographic factors (latitude, longitude, altitude, and distance to bodies of water) obtained from a digital terrain model with a resolution of 90 m were compiled in a database. The equations of the topoclimatic model were generated by a stepwise regression with a backward selection technique. The equations for average monthly temperature, precipitation, and solar radiation were determined by linear combinations. The results were statistically significant with coefficients of determination greater than 90%, in addition to being greater than the existing climate databases for this area.

Assessing habitat suitability and selecting optimal habitats for relict tree Cathaya argyrophylla in Hunan, China: Integrating pollen size, environmental factors, and niche modeling for conservation

The endangered plant Cathaya argyrophylla Chun et Kuang is the only species in the genus and is endemic to China, and because of challenged regeneration and shrinking habitats, the protected areas (PAs) need to be expanded in Hunan Province. In this study, a maximum entropy model (MaxEnt) was used to predict the potentially suitable habitats and population centroid migrations for C. argyrophylla in Hunan under four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) at four time points (2030, 2050, 2070, and 2090) based on thirteen environmental variables. For the first time, optimal habitats that could support breeding Cathaya populations with good fruiting quality in Hunan were identified considering relations between pollen grain size and environmental variables. Of the total area, 10.69 % (22.63 × 103 km2) was suitable for C. argyrophylla, which was distributed primarily in western and southern parts of Hunan. Monthly precipitation in August was the environmental variable that contributed the most (28.2 %) to C. argyrophylla distribution. Other influential variables were annual mean temperature, annual precipitation, mean temperature of the wettest quarter, monthly solar radiation in May, and precipitation of the driest month. The suitable distribution area for C. argyrophylla decreased overall under the climate change scenarios, with SSP2-4.5 the exception. Population centroid of C. argyrophylla shifted overall slightly toward southeast areas with more abundant precipitation in Hunan under climate change. The average shift distance was 3.89 km, and the longest migration distance (8.84 km) was in 2090 under SSP1-2.6. In Hunan, 642.12 km2 were authority identified as optimal habitat based on the current climate. It is proposed that areas with altitudes between 758 m and 1,500 m be treated as crucial PAs networks for C. argyrophylla, mainly including Nanyue, Yanling, Guidong, Xinning, and Zixing counties and districts. The goal of the study was to assist in the conservation and management of C. argyrophylla, but it also provides an example of how to evaluate optimal habitats for those endangered species that need to produce populations with specific superior characteristics.

Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods.

In this study, the predictive power of three different machine learning (ML)-based approaches, namely, multi-gene genetic programming (MGGP), M5 model trees (M5Tree), and K-nearest neighbor algorithm (KNN), for long-term monthly reference evapotranspiration (ET0) prediction were investigated. The input data consist of monthly solar radiation (Rs), maximum air temperature (Tmax), and wind speed (Ws) derived from 163 meteorological stations in Turkey. Different input combinations were created and analyzed. The model's performance was evaluated using criteria such as Nash-Sutcliffe efficiency, Kling-Gupta efficiency, relative root mean squared error, mean absolute percentage error, and determination coefficient. Moreover, Taylor, radar, and boxplot diagrams were created. It was determined that the MGGP model outperformed both the M5Tree and the KNN models. The equation obtained from the MGGP model, for the best-performed combination of Rs-Tmax-Ws, was presented. The best weather conditions were obtained as 0.029 to 31.814MJ/m2, - 5.8 to 45.7°C, and 0.140 to 5.086m/s for Rs, Tmax, and Ws, respectively. It was also found that the Rs was the most potent input variable for ET0 estimation while Ws was the weakest.

A generalized approach for the determination of optimum tilt angle for solar photovoltaic modules with selected locations in Ethiopia as illustration examples

The amount of total solar energy that strikes the photovoltaic module's surface directly affects how much power is generated. On the other hand, solar energy is influenced by the solar module's location, tilt angle, and orientation. The models presented in this study can be used to determine the seasonal and annual optimal tilt angles of photovoltaic modules at any location in Ethiopia as a function of the location's latitude and elevation without the need for solar radiation data. Both isotropic and anisotropic diffuse solar radiation models were used to determine the monthly, seasonal, and annual optimal tilt angles. The monthly average daily global horizontal solar radiation for a total of 44 study sites, 32 for training and 12 for testing, were retrieved from the National Aeronautical and Space Administration database. The optimal tilt angle and regression models were determined by developing and applying algorithms in the programming languages MATLAB and R. The proposed model estimates the optimal tilt angle with the fewest statistical validation errors. By using the proposed monthly optimal tilt angles, solar radiation gains of 6.76% - 8.48% (for isotropic models) and 10.16% - 26.97% (anisotropic models) were obtained for Bahir Dar city. The solar radiation gains for the cities of Adigrat, Butajira, and Gode were found to be 7.18–9.02% (isotropic) and 10.90%–28.06% (anisotropic), 5.43%–7.05% (isotropic) and 8.48%–25.99% (anisotropic), and 4.33%–5.79% (isotropic) and 6.96%–23.03% (anisotropic), respectively. The results of this study were further confirmed by comparing them to previously published papers and online PVGIS and NREL's PVWatt software tools.

A numerical investigation of optimum angles for solar energy receivers in the eastern part of Algeria

Abstract The need to capture the maximum amount of solar energy and to optimize the panels’ collecting surfaces are among the primary objectives of research in solar engineering. The simplest way to accomplish this is to perform a monthly accurate determination of the solar collector’s proper slope and azimuth angles. Indeed, this is the aim of this article, which consists of a graphical optimization based on the Gueymard’s daily integration model. A Matlab program was developed to predict the hourly solar radiation on a solar receiver using the Gueymard model in conjunction with the Liu and Jordan isotropic, Perez, and HDKR anisotropic models. A comprehensive simulation of the monthly solar irradiation throughout 2018 was executed for the city of Annaba, in north–eastern Algeria. The results indicate that the south-facing surface azimuth angle is the most appropriate. In fact, for maximum sunlight capture, the solar collector inclination must be adjusted each month in the range of [10–40°]. Furthermore, the results show that the gains in the amount of solar radiation received throughout the year by the thermal panel mounted at monthly optimum tilt angles are 15.63% in January and 7.37% in July.

Contribution of complementary operation in adapting to climate change impacts on a large-scale wind–solar–hydro system: A case study in the Yalong River Basin, China

Operation flexibility of hydropower stations and regulation ability of reservoirs can complement intermittent wind and photovoltaic power to form a stable wind–solar–hydro complementary system (WSHCS). It is acknowledged that alteration of wind, solar, and water resources by climate change might degrade power generation of WSHCSs; however, comprehensive evaluation of the potential impact of climate change on the energy production, reliability, and power curtailment of WSHCSs remains lacking. Furthermore, quantification of the contribution of complementary operation of a WSHCS in adapting to climate change impacts represents another vital knowledge gap. To address these problems, this study constructed a top-down model chain by integrating global climate models, hydrological models, and energy system operation models. The large-scale WSHCS in the Yalong River Basin (China) was considered as a case study. Results indicated substantial complementarity among the variation of monthly streamflow, wind speed, and solar radiation under a changing climate. The total energy production and reliability of the WSHCS could be guaranteed under most future climate scenarios, while power curtailment risk would increase. Although climate change impacts dominate change in system operation performance, complementary operation can contribute a positive effect to a certain extent, especially for higher reliability and lower power curtailment. The findings indicate the effectiveness of the complementary operation in adapting to climate change impacts on the Yalong River WSHCS. The major novelty of this study is quantification of the contribution of complementary operation in adapting to climate change impacts on WSHCSs, which provides valuable insight regarding evaluation of complementary systems.

Development of Optimal Tilt Angle Models of a Photovoltaic Module for Maximum Power Production: Ethiopia

The power generated from the photovoltaic module is directly related to the magnitude of total incident solar radiation on the surface of the solar module. The total incident solar radiation depends on the location, tilt angle, and orientation of the solar module. In this paper, generic models were developed that determine the seasonal and annual optimal tilt angle of the Photovoltaic module at any location in Ethiopia without using meteorological data. Both isotropic and anisotropic diffuse solar radiation models were used to estimate monthly, seasonal, and annual optimal tilt angles. The monthly average daily global horizontal solar radiation for a total of 44 cities -32 for training and 12 for testing were obtained from the National Aeronautical and Space Administration database, and algorithms were developed and implemented using MATLAB and R programming software to obtain optimum tilt angle and regression models. The study showed that the developed model accurately estimates the optimal tilt angle with the minimum statistical validation errors. It is also found that 5.11% to 6.275% (isotropic) and 5.72% to 6.346% (anisotropic models) solar radiation energy is lost when using the yearly average fixed optimal tilt angle as compared with the monthly optimal tilt angle. The result of this study was also validated by comparing it with the previously published works, PVGIS and PVWatt online software. The graphical abstract is included in the supplementary file.

Estimation of monthly global solar radiation over twelve major cities of Libya

This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.

Analysis of solar photo‐voltaic for grid integration viability in Uganda

AbstractThis study aimed to analyzing grid‐connected solar PV in Uganda for viability by evaluating the performance ratio of the already‐installed solar systems, and how flexible is the grid to accommodate more power from solar. The data collected from the solar plants included array size, type and rating of each module, array output, cell efficiency, DC to AC ratio, systems capital cost per watt, annual maintenance cost, inverter size and efficiency, array tilt, records of monthly solar irradiation, monthly energy produced, capital costs, operational costs and cost of a unit supplied to the grid. The System Advisor Model was used to analyze the technical and economic performances of solar plants in Uganda. The projected solar penetration by the year 2021 was 6.1%. The total annual energy was estimated at 69.52 GWh. The capacity factor ranges from 13.1% to 17.5% and a performance ratio of 0.76, and are within the recommended values. The grid was flexible up to 25.8% to accommodate more solar energy without destabilizing the network. It is viable to invest in solar energy since all four plants showed a positive net present value.

Evaluation of global solar radiation, cloudiness index and sky view factor as potential indicators of Ghana's solar energy resource

The increasing demand of Ghana's renewable energy sector requires a comprehensive evaluation of sky conditions that may be favourable for the efficient harnessing of solar energy resources. However, data for such evaluation are typically scarce due to instrumentation challenges. Empirical estimates using sunshine duration and cloud cover as input data were used to evaluate the solar energy resources’ potential at 22 synoptic stations distributed across the four climatic zones of Ghana (savannah, transition, forest and coastal) for the period 2015 – 2018. Sunshine duration was recorded using the Campbell-Stokes sunshine recorder while the cloud amount was done manually through visual observation. The modified Ångström-Prescot sunshine model was used to estimate the monthly mean global solar radiation. The cloudiness index was then computed as the ratio of the mean cloud cover to the global solar radiation. A modified Steyn-method, based on sunshine duration, was used to estimate the sky view factor. Ordinary kriging was used to interpolate mean quarterly stationary values of the sky condition parameters into spatial distribution over Ghana. Results show that the dry periods (November to April) are characterised by high solar radiations, clear clouds and visible skies as a result of high temperatures, less cloud cover, and low relative humidity, whereas the opposite conditions are observed in the wet periods (May to October). The savannah and coastal zones recorded higher solar radiations with up to 22 MJ m−2 day−1 in the dry season compared to the forest and transition zones. Cloudiness index was above 0.3 and below 0.2 in the wet and dry seasons respectively, for the forest, transition and coastal zones, whereas the index was between 0.15 and 0.3 in the wet season and below 0.1 in the dry season for the savannah zone. Sky view factor was consistently between 0.7 and 0.8 in the dry season, with low values below 0.7 in August for all the climatic zones. Spatial interpolation of the sky condition parameters revealed that the savannah, coastal and eastern transition climatic zones observe high solar radiations, clear and visible skies and are therefore more favourable for harnessing solar energy. These locations may be more appropriate for setting up large-scale solar farms, though dust associated with the Harmattan dry season can be a hinderance.

Analysis and evaluation of solar energy resource change characteristics in the Wanshan Island area

The total solar radiation and insolation hours in Wanshan Island were analyzed by linear regression analysis, distance level analysis method, 5-year sliding average method, and Mann-Kendall test method. The results showed that the total solar radiation in Wanshan Island showed a slight upward trend, and the total monthly solar radiation showed a unimodal distribution, with the highest in July and the lowest in February. The number of annual sunshine hours showed a weak downward trend, and the number of monthly sunshine hours showed a double-peak distribution, with the highest in July and the lowest in March. The analysis and evaluation of solar energy resources in the Wanshan Island area show that the annual average of total solar radiation in the Wanshan Island area is 4,996.25 MJ/m2, belonging to a resource-rich area. The indicator of the stability of solar energy resources is 3.7, which belongs to the resources of more stable areas.

Providing an accurate global model for monthly solar radiation forecasting using artificial intelligence based on air quality index and meteorological data of different cities worldwide.

This study aims to present an exact model for predicting solar radiation worldwide through a general model. In this study, mean monthly global solar radiation would have been predicted by applying artificial intelligence methods including artificial neural network, adaptive neuro-fuzzy inference system and hybrid genetic algorithm for different cities worldwide. Investigating different models under various situations showed that the adaptive neuro-fuzzy inference system created the most accurate and precise model for predicting solar radiation. Statistics indexes, such as the determination coefficient, mean absolute percentage error, root mean square error and mean bias error, for the best model selected are 0.999, 5.50E-04, 5.90E-05 and 0.425, respectively. It can be claimed that according to the amount of the statistical indexes, which was mentioned above, the provided model has approximately more formidable accuracy and credibility in comparison with other models, which other researchers did.

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

Solar radiation is the Earth’s primary source of energy and has an important role in the surface radiation balance, hydrological cycles, vegetation photosynthesis, and weather and climate extremes. The accurate prediction of solar radiation is therefore very important in both the solar industry and climate research. We constructed 12 machine learning models to predict and compare daily and monthly values of solar radiation and a stacking model using the best of these algorithms were developed to predict solar radiation. The results show that meteorological factors (such as sunshine duration, land surface temperature, and visibility) are crucial in the machine learning models. Trend analysis between extreme land surface temperatures and the amount of solar radiation showed the importance of solar radiation in compound extreme climate events. The gradient boosting regression tree (GBRT), extreme gradient lifting (XGBoost), Gaussian process regression (GPR), and random forest models performed better (poor) prediction capabilities of daily and monthly solar radiation. The stacking model, which included the GBRT, XGBoost, GPR, and random forest models, performed better than the single models in the prediction of daily solar radiation but showed no advantage over the XGBoost model in the prediction of the monthly solar radiation. We conclude that the stacking model and the XGBoost model are the best models to predict solar radiation.

TREND ANALYSIS OF EVAPORATION AND SOLAR RADIATION USING INNOVATIVE TREND ANALYSIS METHOD

Analysis of trends in monthly evaporation and solar radiation in the face of climate change gives useful information for better planning and management of water resources. This paper examines the monthly evaporation and solar radiation trend using the recently developed innovative trend analysis method (ITAM). The monthly evaporation trend result shown that 75% of the months indicated decreasing trend with the month of February, March, August and April decreased significant at 0.1%, 10%, 10% and 5% significance level respectively. As regards solar radiation all the months indicated decreasing trend with January, July and October shown a decreasing trend at 5% significant level. By comparing the Mann-Kendall method with the ITAM the reliability of ITAM was ascertained. Hence, ITAM can be effectively utilized in climate change scenarios where useful information is needed for accurate management and planning of water resources.