Evaluation of solar energy potential based on target detection and design of English vocabulary teaching platform

2021 ◽  
Vol 14 (15) ◽  
Author(s):  
Yan Jin
Keyword(s):  
Solar Energy ◽  
Target Detection ◽  
Energy Potential ◽  
Teaching Platform

Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 181
Author(s):  
Imriani Moroki ◽  
Alfrets Septy Wauran
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Moving Average ◽  
Arima Model ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Energy Potential ◽  
Good Design ◽  
Potential Sources ◽  
The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of  the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

Evaluating the spatial and temporal solar energy potential in South Korea

2012 ◽  
Author(s):  
Si-Young Oh ◽  
Jin-Ki Park ◽  
Jong-Hwa Park
Keyword(s):  
Solar Energy ◽  
South Korea ◽  
Energy Potential

scholarly journals Mapping and Estimation of Monthly Global Solar Irradiation in Different Zones in Souss-Massa Area, Morocco, Using Artificial Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
O. Nait Mensour ◽  
S. Bouaddi ◽  
B. Abnay ◽  
B. Hlimi ◽  
A. Ihlal
Keyword(s):  
Solar Energy ◽  
Solar Irradiation ◽  
Kriging Interpolation ◽  
Solar Pv ◽  
Energy Potential ◽  
Global Solar Irradiation ◽  
Interpolation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Solar radiation data play an important role in solar energy research. However, in regions where the meteorological stations providing these data are unavailable, strong mapping and estimation models are needed. For this reason, we have developed a model based on artificial neural network (ANN) with a multilayer perceptron (MLP) technique to estimate the monthly average global solar irradiation of the Souss-Massa area (located in the southwest of Morocco). In this study, we have used a large database provided by NASA geosatellite database during the period from 1996 to 2005. After testing several models, we concluded that the best model has 25 nodes in the hidden layer and results in a minimum root mean square error (RMSE) equal to 0.234. Furthermore, almost a perfect correlation coefficient R=0.988 was found between measured and estimated values. This developed model was used to map the monthly solar energy potential of the Souss-Massa area during a year as estimated by the ANN and designed with the Kriging interpolation technique. By comparing the annual average solar irradiation between three selected sites in Souss-Massa, as estimated by our model, and six European locations where large solar PV plants are deployed, it is apparent that the Souss-Massa area is blessed with higher solar potential.

Solar energy potential in the coastal zone of the Gulf of Mexico

2015 ◽  
Vol 81 ◽  
pp. 534-542 ◽  
Author(s):  
Eunice Villicaña-Ortiz ◽  
Antonio J. Gutiérrez-Trashorras ◽  
José Pablo Paredes-Sánchez ◽  
Jorge Xiberta-Bernat
Keyword(s):  
Solar Energy ◽  
Gulf Of Mexico ◽  
Coastal Zone ◽  
Energy Potential

Preliminary results on the determination of solar energy potential using LiDAR technology

2015 ◽  
Vol 6 (1) ◽  
pp. 11-17 ◽  
Author(s):  
G. Szabó ◽  
P. Enyedi ◽  
Gy. Szabó ◽  
I. Fazekas ◽  
T. Buday ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Energy Production ◽  
Alternative Energy ◽  
Surface Model ◽  
Solar Panels ◽  
Energy Potential ◽  
Potential Surfaces ◽  
Large Power ◽  
Automatic Data

According to the challenge of the reduction of greenhouse gases, the structure of energy production should be revised and the increase of the ratio of alternative energy sources can be a possible solution. Redistribution of the energy production to the private houses is an alternative of large power stations at least in a partial manner. Especially, the utilization of solar energy represents a real possibility to exploit the natural resources in a sustainable way. In this study we attempted to survey the roofs of the buildings with an automatic method as the potential surfaces of placing solar panels. A LiDAR survey was carried out with 12 points/m2 density as the most up-to-date method of surveys and automatic data collection techniques. Our primary goal was to extract the buildings with special regard to the roofs in a 1 km2 study area, in Debrecen. The 3D point cloud generated by the LiDAR was processed with MicroStation TerraScan software, using semi-automatic algorithms. Slopes, aspects and annual solar radiation income of roof planes were determined in ArcGIS10 environment from the digital surface model. Results showed that, generally, the outcome can be regarded as a roof cadaster of the buildings with correct geometry. Calculated solar radiation values revealed those roof planes where the investment for photovoltaic solar panels can be feasible.

Spatial assessment of solar energy potential at global scale. A geographical approach

2019 ◽  
Vol 209 ◽  
pp. 692-721 ◽  
Author(s):  
Remus Prăvălie ◽  
Cristian Patriche ◽  
Georgeta Bandoc
Keyword(s):  
Solar Energy ◽  
Global Scale ◽  
Energy Potential ◽  
Spatial Assessment

scholarly journals Literature Review on the Solar Energy Potential for Botswana

2019 ◽  
Vol 15 (34) ◽  
Author(s):  
Wilbert R. Mutoko ◽  
Phillis Mutoko
Keyword(s):  
Solar Energy ◽  
Literature Review ◽  
Energy Potential

scholarly journals A review of Ghana’s solar energy potential

AIMS Energy ◽  
2016 ◽  
Vol 4 (5) ◽  
pp. 675-696 ◽  
Author(s):  
Samuel Asumadu-Sarkodie ◽  
◽  
Phebe Asantewaa Owusu
Keyword(s):  
Solar Energy ◽  
Energy Potential

scholarly journals TECHNO-ECONOMIC ANALYSIS OF AN OFF-GRID HYBRID ENERGY SYSTEM WITH HOMER PRO

2021 ◽  
Vol 5 (3) ◽  
pp. 56-61
Author(s):  
Ahmet Erhan AKAN
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Economic Analysis ◽  
Energy Conversion ◽  
Renewable Energy Sources ◽  
Lithium Ion ◽  
Energy Sources ◽  
Renewable Energy Resources ◽  
Energy Potential ◽  
Economic Point

The decrease in fossil-based energy sources and increasing environmental problems increase the tendency to renewable energy sources day by day. The potential of renewable energy sources differs according to the region where the energy will be produced. For this reason, it is crucial to conduct a good feasibility study that deals with the selected systems from a technical and economic point of view before making an investment decision on energy conversion systems based on renewable energy sources. In this study, the most suitable equipment and capacities were investigated by examining the techno-economic analysis of a hybrid system created with wind-solar renewable energies for a detached house, which is considered off-grid, in a rural area of Tekirdağ province (40o58.7ı N, 27o30.7ı E). Investigations were carried out using the HOMER Pro (Hybrid Optimization Model for Electric Renewable) program. The wind and solar energy potential of Tekirdağ province were obtained from the NASA renewable energy resources database added to the HOMER Pro program. The daily electricity requirement of the sample house was chosen as 11.27 kWh, and the current peak electrical load was chosen as 2.39 kW. A wind turbine is connected to the AC busbars, solar collectors and battery group connected to the DC busbars, and a converter that converts energy between AC and DC busbars in the energy conversion system. In order to determine the optimum capacities of the system elements, 27486 different simulations were performed by HOMER Pro. The selection of the most suitable system among these was determined according to the lowest net present cost (NPC) value. In addition, the energy production capacities that will occur in the case of different wind speeds were also investigated. Accordingly, the system to be installed with a solar panel with a capacity of 6.25 kW, PV-MPPT with a capacity of 1 kW, 2 wind turbines with a capacity of 1 kW, 8 Lithium-ion batteries with a capacity of 6V-167 Ah, and a converter with a capacity of 2.5 kW has been determined will generate electrical energy of 5433 kWh per year. In addition, it has been determined that 61.8% of this produced energy will be obtained from solar energy and 38.2% from wind energy, and the simple payback period of the investment will be 14 years. It is thought that this study will provide valuable information to researchers and investors.

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