GEOGRAPHICALLY WEIGHTED PRINCIPAL COMPONENTS ANALYSIS APPROACH TO EVALUATE ELECTRICITY CONSUMPTION BEHAVIOUR

  1. TEJEDOR-FLORES, NATHALIA 234
  2. VICENTE-GALINDO, PURIFICACIÓN 1
  3. GALINDO-VILLARDÓN, PURIFICACIÓN 1
  1. 1 Departamento de Estadística, Universidad de Salamanca, Spain
  2. 2 Centro de Investigaciones Hidráulicas e Hidrotécnicas, Universidad Tecnológica de Panamá, Panama
  3. 3 Sistema Nacional de Investigación, Panama
  4. 4 Centro de Estudios Multidisciplinarios en Ciencias, Ingeniería y Tecnología-AIP, Panama
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Actas:
The Sustainable City XIV

ISSN: 1743-3541

ISBN: 9781784664138

Año de publicación: 2020

Páginas: 101-112

Tipo: Aportación congreso

DOI: 10.2495/SC200091 GOOGLE SCHOLAR

Resumen

Electricity consumer behaviour is primarily based on individual decisions, which are often driven by external factors such as economic incentives, existing demographics, environmental variables, social norms, and infrastructure. This study aims to understand the amount of electricity used per hour dedicated in the household (HH) sectors and the paid sectors – including agriculture (AG), other productive sectors (PS) and the service and government sector (SG) – using Geographically Weighted Principal Components Analysis (GWPCA). In the literature, we found that a standard Principal Components Analysis (PCA) can be replaced with a GWPCA when we want to account for a certain spatial heterogeneity. To use the GWPCA to compare the results with a standard PCA, we took the data used in a previous investigation and applied both analyses in order to find a better way to understand the electricity consumer behaviour, using a multivariate analysis. The standard PCA reveals that the first three components collectively account for 73.66% of the variation in the data. Using GWPCA, we found a clear geographical variation in the percentage of total variance data, with higher percentages (90%–95%) located in the south-west and a small part of the north-east of the case of study used. Also, the electrical Energy Throughput in Paid Work (ETPW), and the amount of energy used per hour dedicated to the Paid Work sector (EMRPW), appears to play an important part in defining the local structure in the south-west (coastal region) and in the northern part of the case of study used, respectively. The comparison results suggest that GWPCA provides better fitness than the standard PCA model by considering spatial heterogeneity.

Referencias bibliográficas

  • [1] Bybee, R.W., Planet Earth in crisis: How should science educators respond? The American Biology Teacher, 53(3), pp. 146–153, 1991.
  • [2] Brundtland, G.H., Report of the World Commission on Environment and Development: Our Common Future, The Brundtland Report, 1987.
  • [3] Daly, H.E., Allocation, distribution, and scale: Towards an economics that is efficient, just, and sustainable. Ecological Economics, 6(3), pp. 185–193, 1992.
  • [4] Goodland, R., The concept of environmental sustainability. Annual Review of Ecology, Evolution and Systematics, 26, pp. 1–24, 1995.
  • [5] Berkes, F. & Folke, C., Linking Social and Ecological Systems: Management Practices and Social Mechanisms for Building Resilience, Resilience Alliance: Cambridge, 1998.
  • [6] Vilches, A., Ciencia de la Sostenibilidad: ¿Una nueva disciplina o un nuevo enfoque para todas las disciplinas? Revista Iberoamericana de Educación, 69(1), pp. 39–60, 2015.
  • [7] Kates, R.W. et al., Sustainability science. Science, 292(5517), pp. 641–642, 2001.
  • [8] Flammini, A., Puri, M., Pluschke, L. & Dubois, O., Walking the nexus talk: Assessing the water-energy-food nexus in the context of the sustainable energy for all initiative. https://bit.ly/2XwIvvO. Accessed on: 30 Jul. 2020.
  • [9] Falconí-Benítez, F., Integrated assessment of the recent economic history of Ecuador. Population and Environment, 22(3), pp. 257–280, 2001.
  • [10] Ramos-Martin, J., Giampietro, M. & Mayumi, K., On China’s exosomatic energy metabolism: An application of multi-scale integrated analysis of societal metabolism (MSIASM). Ecological Economics, 63(1), pp. 174–191, 2007.
  • [11] Ramos-Martín, J., Cañellas-Boltà, S., Giampietro, M. & Gamboa, G., Catalonia’s energy metabolism: Using the MuSIASEM approach at different scales. Energy Policy, 37(11), pp. 4658–4671, 2009.
  • [12] Sorman, A.H. & Giampietro, M., Generating better energy indicators: Addressing the existence of multiple scales and multiple dimensions. Ecological Modelling, 223(1), pp. 41–53, 2011.
  • [13] Recalde, M. & Ramos-Martin, J., Going beyond energy intensity to understand the energy metabolism of nations: The case of Argentina. Energy, 37(1), pp. 122–132, 2012.
  • [14] Biggs, E.M. et al., Sustainable development and the water-energy-food nexus: A perspective on livelihoods. Environmental Science and Policy, 54, pp. 389–397, 2015.
  • [15] Giampietro, M., Aspinall, R.J., Ramos-Martin. J. & Bukkens, S.G.F., Resource Accounting for Sustainability Assessment: The Nexus Between Energy, Food, Water and Land Use, 1st ed., Routledge: Abingdon, 2014.
  • [16] Lloyd, C.D., Analysing population characteristics using geographically weighted principal components analysis: A case study of Northern Ireland in 2001. Computers, Environment and Urban Systems, 34(5), pp. 389–399, 2010.
  • [17] Harris, P., Brunsdon, C. & Charlton, M., Geographically weighted principal components analysis. International Journal Geographical Information Science, 25(10), pp. 1717–1736, 2011.
  • [18] Fernández, S., Cotos-Yáñez, T., Roca-Pardiñas, J. & Ordóñez C., Geographically Weighted Principal Components Analysis to assess diffuse pollution sources of soil heavy metal: Application to rough mountain areas in Northwest Spain. Geoderma, 311, pp. 120–129, 2018. The Sustainable City XIV 111 www.witpress.com, ISSN 1743-3541 (on-line) WIT Transactions on Ecology and the Environment, Vol 249, © 2020 WIT Press
  • [19] Gollini, I., Lu, B., Charlton, M., Brunsdon, C. & Harris, P., GWmodel: An R package for exploring spatial heterogeneity. Journal of Statistical Software, 63(17), pp. 1–50, 2015.
  • [20] Villacís, B. & Carrillo, D., Estadística demográfica en el Ecuador: Diagnóstico y propuestas. https://bit.ly/3ieRI3H. Accessed on: 30 Jul. 2020.
  • [21] Tejedor-Flores, N., Vicente-Galindo, P. & Galindo-Villardón, P., Sustainability multivariate analysis of the energy consumption of ecuador using MuSIASEM and BIPLOT approach. Sustainability, 9(6), pp. 1–15, 2017.
  • [22] Ginard-Bosch, F.J. & Ramos-Martín, J., Energy metabolism of the Balearic Islands (1986–2012). Ecological Economics, 124, pp. 25–35, 2016.
  • [23] Wei, C., Cabrera-Barona, P. & Blaschke, T., Local geographic variation of public services inequality: Does the neighborhood scale matter? International Journal of Environmantal Research and Public Health, 13(10), p. 981, 2016.
  • [24] Lu, B., Harris, P., Charlton, M. & Brunsdon, C., The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geospatial Information Science, 17(2), pp. 85–101, 2014.
  • [25] Central Intelligence Agency, Physiography map of Ecudaor. https://bit.ly/3hIzuXK. Accessed on: 30 Jul. 2020.
  • [26] Roca-Pardiñas, J., Ordóñez, C., Cotos-Yáñez, T.R. & Pérez-Álvarez, R., Testing spatial heterogeneity in geographically weighted principal components analysis. International Journal Geographical Information Science, 31(4), pp. 676–693, 2017.
  • [27] Velasco-Fernández, R., Ramos-Martín, J. & Giampietro, M., The energy metabolism of China and India between 1971 and 2010: Studying the bifurcation. Renewable and Sustainable Energy Review, 41(1), pp. 1052–1066, 2015.