Abstract
The developing country like India nowadays relies on the utilization of renewable energy which consist of solar, wind, biomass, hydropower and geothermal energy. Non-renewable energy includes oil, coal, nuclear power and natural gas. The macro-level goal of the undertaken research work is the design and development of big data analytics framework for rural electrification demand planning. The micro-level objective is the geographical mapping and the geospatial energy landscape data visualization of a National Grid’s power transmission line distribution network. This energy landscape data visualization would help the engineers to design the rural electrification plan accordingly based on the geospatial, population density and environmental variables of the proposed energy supply chain network. The proposed decision support system visualizes the existing energy landscape of the power transmission line with substations as nodes and distances between nodes as relationships. The respective data sets required to build the proposed decision support system are collected from the official web sources of Tamil Nadu State Government, India. The explanatory variables are identified from the data set, and feature engineering is done for geospatial energy landscape for data visualization. The IPO cycle (input–process–output cycle) of the system is designed with the identified input variables of energy landscapes. The geospatial latitude and longitude values of the Kodaikanal mountain villages are the input variables for the proposed supply chain network data visualization. The geographical mapping done through the online dynamic maps of energy landscape is shown, and patterns of them are discussed extensively.
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References
Bahaj A, Blunden L (2019) The impact of an electrical mini-grid on the development of a rural community in Kenya. Energies 12:778
Taye BZ, Workineh TG, Nebey AH, Kefale HA (2020) Rural electrification planning using geographic information system. Cogent Eng 7(1):1836730
Yuan X (2020) The application of geographic information system (GIS) in academic success center (ASC) of a medium-sized liberal art university. Educ Res Theory Pract 31(3):94–100
Ran LH, Bo Z (2013) Application of GIS in rural planning. Int J Model Optim 3(2):202–205
Alajangi S, Pyla KR, Eadara A, Prasad NSR (2013) Web GIS based information system for rural development. Int J Sci Res 5(5):2469–2475
Sarkar S (2018) Application of spatial database for rural development and planning in Indian context—a theoretical overview. Int J Res Anal Rev 5(3):131–135
Holguín ES, Chacón RF, Gamarra PS (2019) Sustainable and renewable business model to achieve 100% rural electrification in Perú by 2021. IEEE: 978-1-5386-8218-0/19
Leonard A, Wheeler S, McCulloch M (2020) Geospatial clustering and network design for rural electrification in Africa. IEEE PES/IAS Power Africa: 978-1-7281-6746-6/20
Adkins JE, Modi V, Sherpa S, Han R (2017) A geospatial framework for electrification planning in developing countries. IEEE: 978-1-5090-6046-7/17
Bissiri M, Moura P, Figueiredo NC, da Silva PP (2019) A geospatial approach towards defining cost-optimal electrification pathways in West Africa. Energy
Mentis D, Andersson M, Howells M, Rogner H, Siyal S, Broad O, Korkovelos A, Bazilian M (2016) The benefits of geospatial planning in energy access—a case study on Ethiopia. Applied Geography Elsevier 72(1):1–13
Korkovelos A, Khavari B (2019) The role of open access data in geospatial electrification planning and the achievement of SDG7. An OnSSET-based case study for Malawi. Energies 12:1395
Azad DK, Singh AK (2021) Development of village level geospatial framework for “digital India.” Int J Adv Remote Sens GIS 10(1):3415–3424
López-González A, Ferrer-Martía L, Domenechd B (2019) Sustainable rural electrification planning in developing countries: a proposal for electrification of isolated communities of Venezuela. Energy Policy 129(1):327–338
Perçukua A, Minkovskab D, Stoyanovac L (2018) Big data and time series use in short term load forecasting in power transmission system. Proc Comput Sci 141(1):167–174
Kodaikannal modified master plan of Mountain Villages (Southern Region of Tamilnadu)
Dhanalakshmi J, Ayyanathan N (2019) An implementation of sustainable energy model using multilayer perceptron and EM algorithm. In: National conference on recent trends in computer science and mathematics
Dhanalakshmi J, Ayyanathan N, Pandian NS (2019) Energy analytics and comparative performance analysis of machine learning classifiers on power boiler data set. IEEE
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Dhanalakshmi, J., Ayyanathan, N. (2024). Geospatial Data Visualization of an Energy Landscape and Geographical Mapping of a Power Transmission Line Distribution Network. In: Gunjan, V.K., Kumar, A., Zurada, J.M., Singh, S.N. (eds) Computational Intelligence in Machine Learning. ICCIML 2022. Lecture Notes in Electrical Engineering, vol 1106. Springer, Singapore. https://doi.org/10.1007/978-981-99-7954-7_28
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DOI: https://doi.org/10.1007/978-981-99-7954-7_28
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