scholarly journals Variabilidade Espacial da Radiação Solar na Região de Manaus - AM durante o Experimento GOAmazon 2014/15 (Spatial variability of solar radiation in Manaus-AM region during GOAmazon Experiment 2014/15)

2017 ◽  
Vol 10 (6) ◽  
pp. 1802
Author(s):  
Alice Dos Santos Macedo ◽  
Gilberto Fisch
Keyword(s):  
Solar Radiation ◽  
Spatial Variability ◽  
Rural Areas ◽  
Cloud Cover ◽  
Global Solar Radiation ◽  
Horizontal Gradient ◽  
Strong Impact ◽  
Wet Season ◽  
E 600 ◽  
The City

Este trabalho realizou um estudo da variabilidade espacial em 2014 da irradiância solar na região de Manaus-AM, através de dados observacionais, inserido no Projeto GOAmazon 2014/15. Também, como forma complementar, foi realizada a análise da cobertura de nuvens, chuva e aerossóis e suas interrelações com a irradiância solar. Como resultado, observa-se que os menores valores médios da irradiância solar ocorreram na área urbana,o que é justificado, em parte pela urbanização que altera os fluxos envolvidos, diminuindo a irradiância solar à superfície. Os valores típicos são entre 400 e 600 Wm-2na estação chuvosa (fevereiro/março) com um desvio padrão de 200 e 380 Wm-2e entre 650 e 800 Wm-2na estação seca (agosto-setembro) com desvio padrão de 190 e 300 Wm-2. A B S T R A C T   The solar radiation is an important climatic element in the Amazon region, as it has a strong  impact for ecological (by the preservation of the biodiversity) and technological (as a renewable energy) areas. Consequently, this study contributes for these themes, conducting an observational study of the spatial variability of the solar irradiance for Manaus-AM area, with data collected during the project GOAmazon 2014/15. The cloud cover and rainfall and their relationships with solar radiation were also analysed. The maximum values observed for the wet season (february and march) are in the range 450 and 600 Wm-2 with high standart deviation (300 - 350 Wm-2 ), while they are in the range from 650 and 800 Wm-2  during the dry period (august - september) with lower standart deviation (200 - 300 Wm-2).  These values are directly associated with cloud cover and rainfall. In general, the urban area (Manaus city) showed the low values of solar radiation at the surface compared with its neighborhood, inducing a horizontal gradient of clouds/rainfall and solar energy between the city and the semi rural areas.  Keywords: piranometer, global solar radiation, cloud cover, total sky imager

scholarly journals The seasonal variability of the amount of global solar radiation reaching the ground in urban and rural areas on the example of Warsaw and Belsk

2016 ◽  
Vol 20 (4) ◽  
pp. 29-37
Author(s):  
Kinga Nelken ◽  
Kamil Leziak
Keyword(s):  
Solar Radiation ◽  
Urban Area ◽  
Rural Area ◽  
Rural Areas ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Urban Station ◽  
Reverse Relationship ◽  
Urban And Rural Areas ◽  
The Relationship

AbstractThe aim of this paper is to determine the contemporary differences in the inflow of global solar radiation in Warsaw (urban station) and Belsk (rural station). The meteorological data used comprised daily sums of global solar radiation (in MJ•m−2) and the duration of sunshine (in hours) for the period 2008 2014. On clear days in spring and summer, the rural area receives more solar radiation in comparison to the urban area, whereas in autumn a reverse relationship occurs. On cloudy days in all seasons, the rural area receives more solar radiation than the urban area, and the relationship is the strongest in winter. Differences between urban and rural areas on cloudy days are smaller than those observed on clear days.

Analysis of Experimental Solar Radiation Data for Osogbo, Nigeria

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Abdulhamid Yusuf ◽  
Hakeem Bolarinwa ◽  
Lukman Animasahun ◽  
Yinusa Babatunde
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Total Radiation ◽  
Wet Season ◽  
Annual Average ◽  
Percentage Frequency ◽  
Monthly Average ◽  
Radiation Data ◽  
Maximum Value ◽  
Daily Total

An analysis of measured global solar radiation (GR) in Osogbo (7.77oN, 4.57oE, 288m) is presented in the form of hourly average, monthly average and percentage frequency distribution. The experimental data corresponds to a year data of 2017. The results reveal that the monthly average values of daily total radiation exhibit seasonal variation with maximum value in dry season month of March (16.59MJ/m2) and minimum value in wet season month of August (8.98 MJ/m2). The annual average GR value is 14.20 MJ/m2 while the annual cumulative GR is 5122 MJ/m2. The solar radiation climate of Osogbo has also been compared to those reported for a number of locations. The percentage frequency of days possessing irradiation rate greater than 15 MJ/m2 is 14 percent whereas that possessing less than 10 MJ/m2 is 61 percent. We conclude, based upon the above analysis that Osogbo is characterized by relatively low global solar radiation.

scholarly journals Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Ali Jallal ◽  
◽  
Samira Chabaa ◽  
Abdelouhab Zeroual ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Parameters ◽  
Energy Systems ◽  
Global Solar Radiation ◽  
Acquisition Time ◽  
Feedback Connection ◽  
Intelligent Models ◽  
The City

Precise global solar radiation (GSR) measurements in a given location are very essential for designing and supervising solar energy systems. In the case of rarity or absence of these measurements, it is important to have a theoretical or empirical model to compute the GSR values. Therefore, the main goal of this work is to offer, to designers and engineers of solar energy systems, an appropriate and accurate way to predict the half-hour global solar radiation (HHGSR) time series from some available meteorological parameters (relative humidity, air temperature, wind speed, precipitation, and acquisition time vector in half-hour scale). For that purpose, two intelligent models are developed: the first one is a multivariate dynamic neural network with feedback connection, and the second is a multivariate static neural network. The database used to build these models was recorded in Agdal’s meteorological station in Marrakesh, Morocco, during the years of 2013 and 2014, and it was divided into two subsets. The first subset is used for training and validating the models, and the second subset is used for testing the efficiency and the robustness of the developed models. The obtained results, in terms of the statistical performance indicators, demonstrate the efficiency of the developed forecasting models to accurately predict the HHGSR parameter in the city of Marrakesh, Morocco.

scholarly journals An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

2021 ◽  
Vol 22 (3) ◽  
pp. 1245
Author(s):  
Adi Kurniawan ◽  
Anisa Harumwidiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Percentage Error ◽  
Average Percentage ◽  
Daily Average ◽  
Artificial Neural ◽  
Average Percentage Error ◽  
The City

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

scholarly journals Analysis of Measurements of Direct Normal and Global Solar Radiation for Maceió, Alagoas State, Northeastern Brazil

2020 ◽  
Vol 35 (4) ◽  
pp. 659-674
Author(s):  
José Marcelo Lopes Júnior ◽  
José Leonaldo de Souza ◽  
Ricardo Araujo Ferreira Junior ◽  
Cícero Manoel dos Santos ◽  
Gustavo Bastos Lyra ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Global Solar Radiation ◽  
Northeastern Brazil ◽  
Human Knowledge ◽  
Clearness Index ◽  
Study Region ◽  
Maximum Value ◽  
Sky Conditions

Abstract Studying solar radiation is essential for human knowledge, since it is present in practically all its activities. Thus, the aim of this work was to analyze the climatic and seasonal variation of direct normal and global solar radiation in the region of Maceió, Alagoas State, Northeastern Brazil with sky conditions characterized by clearness index (Kt). The Kt was determined by the ratio between global solar irradiance and solar irradiance at the top of the atmosphere. The highest occurrences of daily direct normal solar irradiance under conditions of Kt ≥ 0.6 were recorded between 400 W m−2 and 700 W m−2 for all seasons. Under conditions of 0.4 ≤ Kt < 0.6, the daily direct normal solar irradiance occurred between 200 W m−2 and 500 W m−2 and for conditions of Kt < 0.4, its maximum value was 200 W m−2. It was observed that the levels of solar incidence in the study region depend on cloud cover conditions, with little influence of seasonality.

Modelling of real solar radiation spatial distribution as a tool for solar energy cadastre in the cities

2017 ◽  
Vol 29 (2) ◽  
pp. 204-215 ◽  
Author(s):  
Małgorzata Pietras-Szewczyk ◽  
Leszek Szewczyk
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Cloud Cover ◽  
Significant Variable ◽  
State Security ◽  
Radiation Distribution ◽  
Linke Turbidity Factor ◽  
Gis Software ◽  
The City

The increasing needs of energy and decreasing traditional energy sources are becoming one of the biggest issues of our civilization. The provision of stable energy supply is a matter of state security. The energy consumption keeps growing especially in big cities. Therefore, it became reasonable to produce energy directly in cities. To optimize the use of the solar energy in the city areas, a fundamental issue is to find and estimate the amount of solar radiations at a specified location by using available tools and data. The main goal of this work is to demonstrate the potential of the r.sun model, a component of GRASS software, in calculating real solar radiation for the selected location. The work starts with demonstrating the effect of cloud cover for the amount of solar radiation reaching the Earth’s surface and the usage of GIS software and Ogimet website in the spatial analysis of real solar radiation distribution. For this purpose, data concerning cloud cover for selected locations were analysed. The study is based on the synoptic data obtained from Ogimet. Average daily cloud cover totals and long-term values were calculated. Apart from the cloud cover data, a significant variable, the Linke turbidity factor, describing the weakening of solar radiation due to the presence of aerosols and water vapour in the atmosphere, was taken into consideration. The obtained results were used to develop a map of real solar radiation distribution for a part of Wrocław. The results obtained by that model with the acquired data by the local meteorological station show compatibility.

scholarly journals Evaluating solar radiation forecast uncertainty

2018 ◽  
Author(s):  
Minttu Tuononen ◽  
Ewan J. O'Connor ◽  
Victoria A. Sinclair
Keyword(s):  
Solar Radiation ◽  
Cloud Cover ◽  
Spatial Scales ◽  
Forecast Error ◽  
Surface Radiation ◽  
Cloud Base ◽  
Strong Impact ◽  
Positive Bias ◽  
Radiative Balance ◽  
Clear Sky

Abstract. The presence of clouds, and their characteristics, has a strong impact on the radiative balance of the Earth and on the amount of solar radiation reaching the Earth's surface. Many applications require accurate forecasts of surface radiation on weather timescales, for example, solar energy and UV radiation forecasts. Here we investigate how operational forecasts of low and mid-level clouds affect the accuracy of solar radiation forecasts. Four years of cloud and solar radiation observations from one site – Helsinki, Finland, are analysed. Cloud observations are obtained from a ceilometer and therefore, we first develop algorithms to reliably detect cloud base, precipitation and fog. These new algorithms are widely applicable for both operational use and research, such as in-cloud icing detection for the wind energy industry and for aviation. The cloud and radiation observations are compared to forecasts from the Integrated Forecast System (IFS) run operationally and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We develop methods to evaluate the skill of the cloud and radiation forecasts. These methods can potentially be extended to hundreds of sites globally. Over Helsinki, the measured Global Horizontal Irradiance (GHI) is strongly influenced by its northerly location and the annual variation in cloudiness. Solar radiation forecast error is therefore larger in summer than in winter, but the relative error in the solar radiation forecast is more or less constant throughout the year. The mean overall bias in the GHI forecast is positive (8 W m−2). The observed and forecast distributions in cloud cover, at the spatial scales we are considering, are strongly skewed towards clear-sky and overcast situations. Cloud cover forecasts show more skill in winter when the cloud cover is predominantly overcast; in summer there are more clear-sky and broken cloud situations. A negative bias was found in forecast GHI for correctly forecast clear-sky cases and a positive bias in correctly forecast overcast cases. Temporal averaging improved the cloud cover forecast and hence decreased the solar radiation forecast error, but made little impact on the overall bias. The positive bias seen in overcast situations occurs when the model cloud has low values of liquid water path (LWP). We attribute this bias to the model having LWP values that are too low or that the model optical properties for clouds with low LWP are incorrect.

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