Skip to main content
SpringerLink
Log in

Cloud computing for energy requirement and solar potential assessment

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://www.solarcloudgis.appspot.com/.

  2. https://www.nrel.gov/international/ra_india.html.

References

  1. Thekaekara, M. P. (1976). Solar radiation measurement: Techniques and instrumentation. Solar Energy, 18(4), 309–325. https://doi.org/10.1016/0038-092X(76)90058-X.

    Article  Google Scholar 

  2. Broesamle, H., Mannstein, H., Schillings, C., & Trieb, F. (2001). Assessment of solar electricity potentials in North Africa based on satellite data and a geographic information system. Solar Energy, 70(1), 1–12. https://doi.org/10.1016/S0038-092X(00)00126-2.

    Article  Google Scholar 

  3. Pandya, M. R., Singh, R. P., Murali, K. R., Babu, P. N., Kirankumar, A. S., & Dadhwal, V. K. (2002). 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), 714–718. https://doi.org/10.1109/TGRS.2002.1000331.

    Article  Google Scholar 

  4. Rigollier, C., Lefèvre, M., & Wald, L. (2004). The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy, 77(2), 159–169. https://doi.org/10.1016/j.solener.2004.04.017.

    Article  Google Scholar 

  5. Wang, S., Anselin, L., Bhaduri, B., Crosby, C., Goodchild, M. F., Liu, Y., et al. (2013). CyberGIS software: a synthetic review and integration roadmap. International Journal of Geographical Information Science, 27(11), 2122–2145. https://doi.org/10.1080/13658816.2013.776049.

    Article  Google Scholar 

  6. Wang, S., & Koch, B. (2010). Determining profits for solar energy with remote sensing data. Energy, 35(7), 2934–2938. https://doi.org/10.1016/j.energy.2010.03.025.

    Article  Google Scholar 

  7. Gueymard, C. A. (2008). REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation—validation with a benchmark dataset. Solar Energy, 82(3), 272–285. https://doi.org/10.1016/j.solener.2007.04.008.

    Article  Google Scholar 

  8. Ramachandra, T. V. (2007). Solar energy potential assessment using GIS. Energy Education Science and Technology, 18(2), 101–114.

    Google Scholar 

  9. Levinson, R., & Akbari, H. (2010). 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), 53–109. https://doi.org/10.1007/s12053-008-9038-2.

    Article  Google Scholar 

  10. Hofierka, J., & Kaňuk, J. (2009). Assessment of photovoltaic potential in urban areas using open-source solar radiation tools. Renewable Energy, 34(10), 2206–2214. https://doi.org/10.1016/j.renene.2009.02.021.

    Article  Google Scholar 

  11. Lukac, N., & Zalik, B. (2013). GPU-based roofs’ solar potential estimation using LiDAR data. Computers & Geosciences, 52, 34–41. https://doi.org/10.1016/j.cageo.2012.10.010.

    Article  Google Scholar 

  12. Polo, J., Bernardos, A., Navarro, A. A., Fernandez-Peruchena, C. M., Ramírez, L., Guisado, M. V., et al. (2015). Solar resources and power potential mapping in Vietnam using satellite-derived and GIS-based information. Energy Conversion and Management, 98, 348–358. https://doi.org/10.1016/j.enconman.2015.04.016.

    Article  Google Scholar 

  13. Rahman, M. H., & Yamashiro, S. (2007). Novel distributed power generating system of PV-ECaSS using solar energy estimation. IEEE Transactions on Energy Conversion, 22(2), 358–367. https://doi.org/10.1109/TEC.2006.870832.

    Article  Google Scholar 

  14. Saha, K. (2016). An object-oriented approach to quantify available roof area for solar PV installation: Case of Bhopal city, Madhya Pradesh, India, 10(2), pp. 133–139.

  15. Su, Y. (2015). A Comparative analysis of the performance of a grid-connected photovoltaic system based on low-and high-frequency solar data. International Journal of Green Energy, 12(12), 1206–1214. https://doi.org/10.1080/15435075.2014.893880.

    Article  Google Scholar 

  16. Suri, M., Huld, T. A., & Dunlop, E. D. (2005). PV-GIS: A web-based solar radiation database for the calculation of PV potential in Europe. International Journal of Sustainable Energy, 24(2), 55–67. https://doi.org/10.1080/14786450512331329556.

    Article  Google Scholar 

  17. Polo, J., Wilbert, S., Ruiz-Arias, J. A., Meyer, R., Gueymard, C., Súri, M., et al. (2016). Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets. Solar Energy, 132, 25–37. https://doi.org/10.1016/j.solener.2016.03.001.

    Article  Google Scholar 

  18. Agarwal, D. (2012). Crayons: An azure cloud based parallel system for GIS overlay operations. In Proceedings—2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012 (pp.1542–1544). https://doi.org/10.1109/sc.companion.2012.315.

  19. Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., et al. (2013). Hadoop-GIS: a high performance spatial data warehousing system over MapReduce. Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, 6(11), 1009–1020. https://doi.org/10.14778/2536222.2536227.

    Google Scholar 

  20. Rajak, R., Raveendran, D., Bh, M. C., & Medasani, S. S. (2015). High resolution satellite image processing using hadoop framework. In 2015 IEEE international conference on cloud computing in emerging markets (CCEM) (pp. 16–21). https://doi.org/10.1109/ccem.2015.16.

  21. Shaheen, M., & Potdar, M. B. (2017). Image processing in hadoop distributed environment. 2, pp. 188–195.

  22. Nikam, P. P., & Suryawanshi, R. S. (2015). Microsoft windows azure: Developing applications for highly available storage of cloud. International Journal of Science and Research, 4(12), 662–665.

    Google Scholar 

  23. Qazi, N., Smyth, D., & McCarthy, T. (2013). Towards a GIS-based decision support system on the amazon cloud for the modelling of domestic wastewater treatment solutions in Wexford, Ireland. In Proceedings—UKSim 15th international conference on computer modelling and simulation, UKSim 2013 (pp. 236–240). https://doi.org/10.1109/uksim.2013.62.

  24. Cossu, R., Di Giulio, C., Brito, F., & Petcu, D. (2015). Cloud computing for Earth OBSERVATION. Data Intensive Storage Services for Cloud Environments. https://doi.org/10.4018/978-1-4666-3934-8.ch012.

    Google Scholar 

  25. Piragnolo, M., Masiero, A., Fissore, F., & Pirotti, F. (2015). Solar irradiance modelling with NASA WW GIS environment. ISPRS International Journal of Geo-Information, 4(2), 711–724. https://doi.org/10.3390/ijgi4020711.

    Article  Google Scholar 

  26. Kazantzidis, A., Nikitidou, E., Salamalikis, V., Tzoumanikas, P., & Zagouras, A. (2017). New challenges in solar energy resource and forecasting in Greece. International Journal of Sustainable Energy. https://doi.org/10.1080/14786451.2017.1280495.

    Google Scholar 

  27. Gueymard, C. A. (2010). Progress in direct irradiance modeling and validation. In The solar 2010 conference, (May).

  28. Taylor, M., Kosmopoulos, P. G., Kazadzis, S., Keramitsoglou, I., & Kiranoudis, C. T. (2016). Neural network radiative transfer solvers for the generation of high resolution solar irradiance spectra parameterized by cloud and aerosol parameters. Journal of Quantitative Spectroscopy & Radiative Transfer, 168, 176–192. https://doi.org/10.1016/j.jqsrt.2015.08.018.

    Article  Google Scholar 

  29. QGIS Development Team. (2017). Retrieved 17 January 2017, from https://qgis.org/en/site/.

  30. Esri Development Team. (2017). Retrieved 26 November 2016, from https://www.arcgis.com/features/index.html.

  31. Erdas Imagine Hexagon Geospatial Development Team. (2016). Retrieved 16 October 2016, from https://www.hexagongeospatial.com/products/power-portfolio/erdas-imagine.

  32. Eclipse Neon Development Team. (2016). Retrieved 20 June 2016, from https://www.eclipse.org/neon/.

  33. GeoTools The Open Source Java GIS Toolkit—GeoTools. (2017). Open Geospatial Consortium (OGC). Retrieved 30 January 2017, from http://www.geotools.org/.

  34. Google App Engine Plugin for Eclipse. (2017). Retrieved 20 June 2017, from https://cloud.google.com/eclipse/docs/migrating-gpe.

  35. Vitolo, C., Elkhatib, Y., Reusser, D., Macleod, C. J. A., & Buytaert, W. (2015). Web technologies for environmental Big Data. Environmental Modelling and Software, 63, 185–198. https://doi.org/10.1016/j.envsoft.2014.10.007.

    Article  Google Scholar 

  36. Sanderson, D. (2012). In M. Loukides, & M. Blanchette (Eds.) Programming Google App Engine, 2nd edn. Sebastopol: O’Reilly Media.

  37. Sukhatme, S. P., & Nayak, J. K. (2008). Solar energy: Principles of thermal collection and storage (3rd ed.). New Delhi: Tata McGraw-Hill Education.

    Google Scholar 

  38. Muneer, T., Zhang, X., & Wood, J. (2002). Evaluation of an innovative sensor for measuring global and diffuse irradiance, and sunshine duration. International Journal of Solar Energy, 22(3–4), 115–122. https://doi.org/10.1080/0142591032000069792.

    Article  Google Scholar 

  39. Teske, S., Pregger, T., Simon, S., Naegler, T., Graus, W., & Lins, C. (2011). Energy [R]evolution 2010-a sustainable world energy outlook. Energy Efficiency, 4(3), 409–433. https://doi.org/10.1007/s12053-010-9098-y.

    Article  Google Scholar 

  40. Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017). Exploring Google earth engine platform for Big Data Processing: Classification of multi-temporal satellite imagery for crop mapping. Frontiers in Earth Science, 5(February), 1–10. https://doi.org/10.3389/feart.2017.00017.

    Google Scholar 

Download references

Acknowledgements

This research work has been supported financially during the doctoral course of the first author by Ministry of Human Resource Development, Government of India. The authors would like to thank the Editor-in-Chief Jung-Sup Um and the anonymous reviewers for getting the manuscript in this form. Their suggestions helped in improving the technicality and structure of the manuscript.

Author information

Authors and Affiliations

  1. Geomatics Engineering Group, CED, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Mudit Kapoor & Rahul Dev Garg

Authors
  1. Mudit Kapoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahul Dev Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mudit Kapoor.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (RAR 45,169 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kapoor, M., Garg, R.D. Cloud computing for energy requirement and solar potential assessment. Spat. Inf. Res. 26, 369–379 (2018). https://doi.org/10.1007/s41324-018-0181-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41324-018-0181-3

Keywords

Search

Navigation