scholarly journals Potencial/Aproveitamento de Energia Solar e Eólica no Semiárido Nordestino: Um Estudo de Caso em Juazeiro – BA nos Anos de 2000 a 2009 (Potencial/Usage of Wind and Solar Energy in the Northeastern Semiarid: A Study in Juazeiro-BA Between 2000 and 2009)

2012 ◽  
Vol 5 (3) ◽  
pp. 586
Author(s):  
Glauciene Justino Ferreira da Silva ◽  
Thiago Emmanuel Araújo Severo
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Natural Resources ◽  
Wind Power ◽  
Meteorological Data ◽  
Clean Energy ◽  
Renewable Sources ◽  
Renewable Sources Of Energy ◽  
Brazilian Semiarid ◽  
The Way

Com a escassez dos recursos naturais cada vez mais crescente, tem-se intensificado a busca por novas alternativas de gerar energia limpa, segura e que atenda as necessidades da população. Nesse contexto, a utilização da energia solar e eólica surge como uma opção viável para modificar a forma como se gera energia. Visto à localização privilegiada do semiárido brasileiro, a procura pela utilização de fontes renováveis para a produção de energia elétrica viabiliza estudos e diagnósticos para se conhecer o potencial eólico e solar da região. Partindo desse pressuposto, esse trabalho procurou demonstrar o potencial de energia solar e eólica de Juazeiro-BA, por meio de dados meteorológicos. Esta região possui ventos com maiores velocidades nos meses de agosto (247,4 km/dia) e setembro (241,6 km/dia), mantendo uma média de 195,8 km/dia ao longo dos 10 anos analisados. Os valores de irradiação solar mostram que é possível um aproveitamento médio de 2285,653 Wh/m².dia considerando-se apenas a irradiação direta.Palavras chave: Energias Limpas, Radiação Solar, Vento Potencial/Usage of Wind and Solar Energy in the Northeastern Semiarid: A Study in Juazeiro-BA Between 2000 and 2009 ABSTRACTThe scarcity of natural resources are increasingly growing, and has intensified the search for new alternatives to generate clean and safe ways to attend the needs of the population. The use of solar and wind based energy comes into scene as a viable and intelligent option to change the way energy is generated. Seen the privileged location of the Brazilian semiarid, there’s a demand for the use of renewable sources of energy and studies to measure the potential of wind and solar energy in this region. Under this study aimed to demonstrate the potentials of solar and wind power in Juazeiro-BA through meteorological data. This region harbors winds with higher velocities in the months of August (247.4 km / day) and September (241.6 km / day), maintaining an average of 195.8 km / day over the 10 analyzed years. The values of solar radiation shows that it is possible to use an average of 2285.653 Wh / m². Day considering only the direct irradiation.Keywords: Clean energy, Solar Radiation, Wind

Application of Renewable Energy in the Construction

2012 ◽  
Vol 193-194 ◽  
pp. 111-114 ◽  
Author(s):  
Yue Ren ◽  
Zhi Qi
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Energy Conservation ◽  
Geothermal Energy ◽  
Renewable Sources ◽  
Renewable Sources Of Energy ◽  
Integrated Project

We discuss the form of application of renewable sources of energy including solar energy and geothermal energy in the environment of construction, and an integrated project on renewable sources of energy is taken as a case study. We also analyze the feasible plans that utilize multiple renewable sources of energy in the construction. The significance of the energy conservation and reduction is presented as well.

Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications

2013 ◽  
pp. 376-436 ◽  
Author(s):  
Radian Belu
Keyword(s):  
Artificial Intelligence ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Energy Systems ◽  
Poor Quality ◽  
Quality Of Data ◽  
Information Patterns ◽  
Time Requirements

Artificial intelligence (AI) techniques play an important role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms employed to model, control, or to predict performances of the energy systems are complicated involving differential equations, large computer power, and time requirements. Instead of complex rules and mathematical routines, AI techniques are able to learn the key information patterns within a multidimensional information domain. Design, control, and operation of solar energy systems require long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer of a number of shortcomings (e.g. poor quality of data, insufficient long series, etc.). To overcome these problems AI techniques appear to be one of the strongest candidates. The chapter provides an overview of commonly used AI methodologies in solar energy, with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. Selected AI applications to solar energy are outlined in this chapter. In particular, methods using the AI approach for the following applications are discussed: prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

scholarly journals Solar Energy Potential in Turkey

2005 ◽  
Vol 23 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Havva Balat
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Geographical Location ◽  
Clean Energy ◽  
Energy Resources ◽  
Solar Thermal ◽  
Energy Potential ◽  
Yearly Average ◽  
Solar Thermal Applications

In this study, the solar energy potential of Turkey was investigated. Among the alternative clean energy resources in Turkey, the most important one is solar energy. Turkey's solar energy potential has been estimated to be 26.4 million toe as thermal and 8.8 million toe as electricity. Generally, solar energy is used for heating and the consumption of solar energy has increased from 5 ktoe in 1986 to 335 ktoe in 2003. Turkey's geographical location is highly favourable for utilization of solar energy. The yearly average solar radiation is 3.6 kWh/(m2 day) and the total yearly insulation period is approximately 2460 hours, which is sufficient to provide adequate energy for solar thermal applications.

scholarly journals Solar Energy Technology for Northern Cyprus: Assessment, Statistical Analysis, and Feasibility Study

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 940 ◽  
Author(s):  
Youssef Kassem ◽  
Hüseyin Çamur ◽  
Salman Mohammed Awadh Alhuoti
Keyword(s):  
Power Generation ◽  
Solar Radiation ◽  
Solar Energy ◽  
Solar Power ◽  
Ghg Emissions ◽  
Clean Energy ◽  
Global Solar Radiation ◽  
Feasibility Analysis ◽  
Energy Technology ◽  
Northern Cyprus

Solar power is the fastest-growing energy source in the world. New technologies can help to generate more power from solar energy. The present paper aims to encourage people and the government to develop solar energy-based power projects to achieve sustainable energy infrastructures, especially in developing countries. In addition, this paper presents a solar energy road map to attract investors to invest in clean energy technology to help reduce the effect of global warming and enhance sustainable technological development. Therefore, the first objective of the paper is to analyze and compare the monthly global solar radiation for five different locations in Northern Cyprus using the measured data collected from the Meteorological Department and estimated values collected from the satellite imagery database. In addition, the mean hourly meteorological parameters including global solar radiation, air temperature, sunshine, and relative humidity are analyzed statistically and the type of distribution functions are selected based on skewness and kurtosis values. Accordingly, estimating global solar radiation improves solar power generation planning and reduces the cost of measuring. Therefore, models of a surface were analyzed by means of polynomial adjustments considering the values of R-squared. Finally, this study provides a comprehensive and integrated feasibility analysis of a 100 MW grid-connected solar plant project as an economic project in the selected region to reduce electricity tariffs and greenhouse gas (GHG) emissions. RETScreen Expert software was used to conduct the feasibility analysis in terms of energy production, GHG emissions, and financial parameters for the best location for the installation of a 100 MW grid-connected photovoltaic (PV) plant. Finally, the results concluded that the proposed solar system could be used for power generation in Northern Cyprus.

scholarly journals Global solar radiation modeling using different machine learning and empirical models in Northeast China

2021 ◽  
Author(s):  
Yue Jia ◽  
Yongjun Su ◽  
Fengchun Wang ◽  
Pengcheng Li ◽  
Shuyi Huo
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Solar Energy ◽  
Northeast China ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Empirical Models ◽  
Learning Models ◽  
Machine Learning Models

Abstract Reliable global solar radiation (Rs) information is crucial for the design and management of solar energy systems for agricultural and industrial production. However, Rs measurements are unavailable in many regions of the world, which impedes the development and application of solar energy. To accurately estimate Rs, this study developed a novel machine learning model, called a Gaussian exponential model (GEM), for daily global Rs estimation. The GEM was compared with four other machine learning models and two empirical models to assess its applicability using daily meteorological data from 1997–2016 from four stations in Northeast China. The results showed that the GEM with complete inputs had the best performance. Machine learning models provided better estimates than empirical models when trained by the same input data. Sunshine duration was the most effective factor determining the accuracy of the machine learning models. Overall, the GEM with complete inputs had the highest accuracy and is recommended for modeling daily Rs in Northeast China.

scholarly journals Analyzing territory for the sustainable development of solar photovoltaic power using GIS databases

2019 ◽  
Vol 191 (12) ◽  
Author(s):  
Inmaculada Guaita-Pradas ◽  
Inmaculada Marques-Perez ◽  
Aurea Gallego ◽  
Baldomero Segura
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Site Selection ◽  
Electric Energy ◽  
Clean Energy ◽  
The Sustainable Development ◽  
Selection Processes ◽  
Individual Site ◽  
Solar Radiation Intensity ◽  
Energy Production System

AbstractSolar energy generated by grid-connected photovoltaic (GCPV) systems is considered an important alternative electric energy source because of its clean energy production system, easy installation, and low operating and maintenance costs. This has led to it becoming more popular compared with other resources. However, finding optimal sites for the construction of solar farms is a complex task with many factors to be taken into account (environmental, social, legal and political, technical-economic, etc.), which classic site selection models do not address efficiently. There are few studies on the criteria that should be used when identifying sites for solar energy installations (large grid-connected photovoltaic systems which have more than 100 kWp of installed capacity). It is therefore essential to change the way site selection processes are approached and to seek new methodologies for location analysis. A geographic information system (GIS) is a tool which can provide an effective solution to this problem. Here, we combine legal, political, and environmental criteria, which include solar radiation intensity, local physical terrain, environment, and climate, as well as location criteria such as the distance from roads and the nearest power substations. Additionally, we use GIS data (time series of solar radiation, digital elevation models (DEM), land cover, and temperature) as further input parameters. Each individual site is assessed using a unique and cohesive approach to select the most appropriate locations for solar farm development in the Valencian Community, a Spanish region in the east of Spain.

scholarly journals Solar Radiation: Models and Measurement Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
C. K. Pandey ◽  
A. K. Katiyar
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Measurement Techniques ◽  
Research Work ◽  
Diffuse Radiation ◽  
Geometrical Parameters ◽  
Efficient Utilization ◽  
Energy Potential

In order to grasp the significance of the work accomplished by the author, it is necessary to keep abreast of the present developments in this field. The research work reported in the paper is an attempt to get knowledge to assess the solar energy potential for practical and efficient utilization in India. Our work is centered on estimating realistic values of solar (global and diffuse) radiation on horizontal and tilted surfaces using measured meteorological data and geographical and geometrical parameters for India.

Models for Determination of Solar Energy Potential

2008 ◽  
Vol 26 (5) ◽  
pp. 281-292 ◽  
Author(s):  
Kadir Bakirci
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Global Radiation ◽  
Statistical Error ◽  
Mean Bias Error ◽  
Bias Error ◽  
Energy Potential ◽  
Monthly Average ◽  
Correlation Models

Many solar energy systems require monthly average meteorological data as a basis for design. In the study, it was investigated whether existing solar radiation models are suitable to determine the values of solar radiation, and new empirical equations were developed which correlate the monthly average daily global radiation using sunshine hour data. The correlation models were compared by using statistical error tests such as mean bias error (MBE) and root mean square error (RMSE). The new correlation models demonstrate close agreement with the observed data. An application of this study was performed using the experimental data measured for Erzurum, Turkey.

scholarly journals Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications

Robotics ◽  
2013 ◽  
pp. 1662-1720 ◽  
Author(s):  
Radian Belu
Keyword(s):  
Artificial Intelligence ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Energy Systems ◽  
Poor Quality ◽  
Quality Of Data ◽  
Information Patterns ◽  
Time Requirements

Artificial intelligence (AI) techniques play an important role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms employed to model, control, or to predict performances of the energy systems are complicated involving differential equations, large computer power, and time requirements. Instead of complex rules and mathematical routines, AI techniques are able to learn the key information patterns within a multidimensional information domain. Design, control, and operation of solar energy systems require long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer of a number of shortcomings (e.g. poor quality of data, insufficient long series, etc.). To overcome these problems AI techniques appear to be one of the strongest candidates. The chapter provides an overview of commonly used AI methodologies in solar energy, with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. Selected AI applications to solar energy are outlined in this chapter. In particular, methods using the AI approach for the following applications are discussed: prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

scholarly journals A GIS Open Source Software application for mapping solar energy resources in urban areas

2019 ◽  
Vol 116 ◽  
pp. 00060
Author(s):  
Małgorzata Pietras-Szewczyk
Keyword(s):  
Spatial Distribution ◽  
Solar Radiation ◽  
Solar Energy ◽  
Open Source ◽  
Urban Areas ◽  
Meteorological Data ◽  
Energy Resources ◽  
Geographical Information ◽  
Software Module ◽  
The Real

This paper presents the development of an open source geographical information system (GIS) software module for mapping solar energy resources for urban areas. The main goal of this work is to demonstrate the potential use of the r.sun module, a component of GRASS software, in calculating real solar radiation for urban areas. Modelling of the spatial distribution of solar radiation is one of the program functions. The r.sun module is dedicated for that purpose; however, it can only generate the spatial distribution of potential solar radiation. To get the real solar radiation maps it is advisable to use meteorological data, that describe diminution of solar radiation caused by cloud cover. In order to facilitate the generation of maps a GRASS source code modification was made. As a result, the r.sun module used in this work generates the real spatial distribution of solar radiation. The results are shown to be comparable with solar radiation satellite data obtained from the HelioClim project.

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