Cloud computing for energy requirement and solar potential assessment

被引:3
作者
Kapoor M. [1 ]
Garg R.D. [1 ]
机构
[1] Geomatics Engineering Group, CED, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand
关键词
Eclipse IDE; Energy utilization; GAE; GHI; Remote sensing; Solar potential;
D O I
10.1007/s41324-018-0181-3
中图分类号
学科分类号
摘要
The objective of this research is to derive an approach for the assessment of solar potential using cloud computing for a better energy planning. This approach is used to calculate energy requirement and solar potential having precise prediction probability. The energy requirement has been calculated based on the inputs such as the number of fans, tube lights, and electric pump with their wattage and usage hours. The assessment of the solar potential is based on the input parameters such as Global Horizontal Irradiance (GHI), sunshine hours, India Meteorological Department (IMD) data, cloud cover, tilted irradiance, etc. It is executed by software developed in Eclipse IDE (Integrated Development Environment), an open-source toolkit. Perl script has been used to convert the GHI onto the tilted surface which efficiently quantifies the collected solar irradiance. The IMD data are used for predicting the number of cloudy and rainy days for further estimation of solar potential. Cloud computing is used in uploading of the software module on the cloud. Google App Engine is used to deploy project information on the cloud. It has been found that enough solar potential is available to install Solar Photovoltaic (SPV) modules at Meerpur, India using software tool developed. © 2018, Korean Spatial Information Society.
引用
收藏
页码:369 / 379
页数:10
相关论文
共 40 条
[1]  
Thekaekara M.P., Solar radiation measurement: Techniques and instrumentation, Solar Energy, 18, 4, pp. 309-325, (1976)
[2]  
Broesamle H., Mannstein H., Schillings C., Trieb F., Assessment of solar electricity potentials in North Africa based on satellite data and a geographic information system, Solar Energy, 70, 1, pp. 1-12, (2001)
[3]  
Pandya M.R., Singh R.P., Murali K.R., Babu P.N., Kirankumar A.S., Dadhwal V.K., Bandpass solar exoatmospheric irradiance and Rayleigh optical thickness of sensors on board Indian remote sensing satellites-1B, -1C, -1D, and P4, IEEE Transactions on Geoscience and Remote Sensing, 40, 3, pp. 714-718, (2002)
[4]  
Rigollier C., Lefevre M., Wald L., The method Heliosat-2 for deriving shortwave solar radiation from satellite images, Solar Energy, 77, 2, pp. 159-169, (2004)
[5]  
Wang S., Anselin L., Bhaduri B., Crosby C., Goodchild M.F., Liu Y., Nyerges T.L., CyberGIS software: a synthetic review and integration roadmap, International Journal of Geographical Information Science, 27, 11, pp. 2122-2145, (2013)
[6]  
Wang S., Koch B., Determining profits for solar energy with remote sensing data, Energy, 35, 7, pp. 2934-2938, (2010)
[7]  
Gueymard C.A., REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation—validation with a benchmark dataset, Solar Energy, 82, 3, pp. 272-285, (2008)
[8]  
Ramachandra T.V., Solar energy potential assessment using GIS, Energy Education Science and Technology, 18, 2, pp. 101-114, (2007)
[9]  
Levinson R., Akbari H., Potential benefits of cool roofs on commercial buildings: Conserving energy, saving money, and reducing emission of greenhouse gases and air pollutants, Energy Efficiency, 3, 1, pp. 53-109, (2010)
[10]  
Hofierka J., Kanuk J., Assessment of photovoltaic potential in urban areas using open-source solar radiation tools, Renewable Energy, 34, 10, pp. 2206-2214, (2009)