Abstract
This article is a methodical attempt to understand the factors that influence energy consumption in households in the mountainous settlement of Metsovo, Greece. So far, most of the research on the settlement has indirectly approached the investigation of the factors that shape the energy behavior of households. In the present research, the identification of factors is directly approached through linear regression and clustering methods. Income, heating system, and household size were identified as the main factors influencing household energy expenditure. Since mountain areas are plagued by energy poverty, the study of household energy behavior inevitably highlights aspects of this phenomenon. By highlighting these factors and the spatial dimension of energy consumption (i.e., higher thermal energy needs in mountain areas), it was possible to suggest more targeted measures specifically designed for mountain areas, complementing the existing energy policy.
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Greece 2023, Energy Policy Review of the International Energy Agency (IEA). https://www.iea.org/reports/greece-2023
Liquid Fuel Prices Observatory: http://www.fuelprices.gr/deltia.view
Electricity prices, first semester of 2017–2019 (EUR per kWh): https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Electricity_prices,_first_semester_of_2017-2019_(EUR_per_kWh).png
Wood Pellets and modern appliances: a time-reliable combination of renewable and economical heating: https://www.energymag.gr/energeia/90806_pelletes-xyloy-wood-pellets-kai-syghrones-syskeyes-enas-diahronika-axiopistos
References
Akay, Ö., & Yüksel, G. (2018). Clustering the mixed panel dataset using Gower’s distance and k-prototypes algorithms. Communications in Statistics-Simulation and Computation, 47(10), 3031–3041. https://doi.org/10.1080/03610918.2017.1367806
Al-Ghandoor, A. J. J. O., Jaber, J. O., Al-Hinti, I., & Mansour, I. M. (2009). Residential past and future energy consumption: Potential savings and environmental impact. Renewable and Sustainable Energy Reviews, 13(6–7), 1262–1274. https://doi.org/10.1016/j.rser.2008.09.008
Ali, S. S. S., Razman, M. R., Awang, A., Asyraf, M. R. M., Ishak, M. R., Ilyas, R. A., & Lawrence, R. J. (2021). Critical determinants of household electricity consumption in a rapidly growing city. Sustainability, 13(8), 4441. https://doi.org/10.3390/su13084441
Assimakopoulos, V., & Domenikos, H. G. (1991). Consumption preferences structure of Greek households. Energy Economics, 13(3), 163–167. https://doi.org/10.1016/0140-9883(91)90017-T
Auffhammer, M., & Mansur, E. T. (2014). Measuring climatic impacts on energy consumption: A review of the empirical literature. Energy Economics, 46, 522–530. https://doi.org/10.1016/j.eneco.2014.04.017
Balaskas, A., Papada, L., Katsoulakos, N., Damigos, D., & Kaliampakos, D. (2021). Energy poverty in the mountainous town of Metsovo Greece. Journal of Mountain Science, 18(9), 2240–2254. https://doi.org/10.1007/s11629-020-6436-1
Bedir, M., Hasselaar, E., & Itard, L. (2013). Determinants of electricity consumption in Dutch dwellings. Energy and Buildings, 58, 194–207. https://doi.org/10.1016/j.enbuild.2012.10.016
Boardman, B. (1991). Fuel poverty is different. Policy Studies, 12(4), 30–41. https://doi.org/10.1080/01442879108423600
Boemi, S. N., Avdimiotis, S., & Papadopoulos, A. M. (2017). Domestic energy deprivation in Greece: A field study. Energy and Buildings, 144, 167–174. https://doi.org/10.1016/j.enbuild.2017.03.009
Botchkarev, A. (2018). Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology. arXiv preprint arXiv:1809.03006. https://doi.org/10.48550/arXiv.1809.03006
Botetzagias, I., Malesios, C., & Poulou, D. (2014). Electricity curtailment behaviors in Greek households: Different behaviors, different predictors. Energy Policy, 69, 415–424. https://doi.org/10.1016/j.enpol.2014.03.005
Büyükalaca, O., Bulut, H., & Yılmaz, T. (2001). Analysis of variable-base heating and cooling degree-days for Turkey. Applied Energy, 69(4), 269–283. https://doi.org/10.1016/S0306-2619(01)00017-4
Chatzikonstantinou, E., Katsoulakos, N., & Vatavali, F. (2022). Housing and energy consumption in Greece. Households’ experiences and practices in the context of the energy crisis. In IOP Conference Series: Earth and Environmental Science, 1123(1), 012043. https://doi.org/10.1088/1755-1315/1123/1/012043
Chen, Y., Guo, M., Chen, Z., Chen, Z., & Ji, Y. (2022). Physical energy and data-driven models in building energy prediction: A review. Energy Reports, 8, 2656–2671. https://doi.org/10.1016/j.egyr.2022.01.162
Dent, I., Craig, T., Aickelin, U., & Rodden, T. (2014). Variability of behaviour in electricity load profile clustering; Who does things at the same time each day?. In Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16–20, 2014. Proceedings 14 (pp. 70–84). Springer International Publishing. https://doi.org/10.1007/978-3-319-08976-8_6
Dorman, K. S., & Maitra, R. (2022). An efficient k-modes algorithm for clustering categorical datasets. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(1), 83–97. https://doi.org/10.1002/sam.11546
Dubey, A. K., Kumar, A., García-Díaz, V., Sharma, A. K., & Kanhaiya, K. (2021). Study and analysis of SARIMA and LSTM in forecasting time series data. Sustainable Energy Technologies and Assessments, 47, 101474. https://doi.org/10.1016/j.seta.2021.101474
ΕIA,International Energy Outlook 2021 U.S. Energy Information Administration, Washington D.C, USA (2021). (Accessed on 22/06/2023)
Eren, B. M., Taspinar, N., & Gokmenoglu, K. K. (2019). The impact of financial development and economic growth on renewable energy consumption: Empirical analysis of India. Science of the Total Environment, 663, 189–197. https://doi.org/10.1016/j.scitotenv.2019.01.323
Eurostat (2020) EU statistics on income and living conditions (EU-SILC) methodology – economic strain. <http://ec.europa.eu/eurostat/statistics-explained/index.php/EU_statistics_on_income_and_living_conditions_(EU-SILC)_methodology_-_economic_strain#Main_tables> (Accessed on 25/05/2023)
Eurostat, 2020, Inability to keep home adequately warm - EU-SILC survey, https://ec.europa.eu/eurostat/databrowser/view/ilc_mdes01/default/table?lang=en,(accessed on:23/12/2020)
Eurostat (2023) Energy consumption in households. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_consumption_in_households (Accessed on 25/06/2023)
Evans, W. (2010). Durbin-Watson significance tables. University of Notre Dame
Filippini, M., & Pachauri, S. (2004). Elasticities of electricity demand in urban Indian households. Energy Policy, 32(3), 429–436. https://doi.org/10.1016/S0301-4215(02)00314-2
Funnell, D., & Parish, R. (2005). Mountain environments and communities. Routledge.
Gatsonis, C., & Sampson, A. R. (1989). Multiple correlation: exact power and sample size calculations. Psychological Bulletin, 106(3), 516. https://doi.org/10.1037/0033-2909.106.3.516
Gohari, A., Matori, N., Yusof, K. W., Toloue, I., & Myint, K. C. (2018). Effects of the fuel price increase on the operating cost of freight transport vehicles. In E3S Web of Conferences, 34, 01022. https://doi.org/10.1051/e3sconf/20183401022
González-Aguilera, D., Lagueela, S., Rodríguez-Gonzálvez, P., & Hernández-López, D. (2013). Image-based thermographic modeling for assessing energy efficiency of buildings façades. Energy and Buildings, 65, 29–36. https://doi.org/10.1016/j.enbuild.2013.05.040
Greek Thermal Insulation Regulation of Buildings, 362/Δ, 4.7.1979. (Accessed on 25/05/2023)
Guenoukpati, A., Salami, A. A., Birregah, B., & Bakpo, Y. A (2021) A Novel Approach for Electric Load Prediction Using Convolutional Lstms Networks with Sorted Wavelet Transform Coefficient. Available at SSRN 4775353. https://doi.org/10.2139/ssrn.4775353
Hellenic Statistical Authority, 2011. Cencus of population – residences 2011. http://www.statistics.gr/el/2011-census-pop-hous (Accessed on 22/04/2023)
Hellenic Statistical Authority. (2013). Press Release, Research of energy consumption in households, 2011-2012. https://www.statistics.gr/documents/20181/e74d6134-8c02-404e-a02baa6d959219e3. Accessed 22 Jun 2023.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 283–304.
Huang, W. H. (2015). The determinants of household electricity consumption in Taiwan: Evidence from quantile regression. Energy, 87, 120–133. https://doi.org/10.1016/j.energy.2015.04.101
Kandananond, K. (2011). Forecasting electricity demand in Thailand with an artificial neural network approach. Energies, 4(8), 1246–1257. https://doi.org/10.3390/en4081246
Karani, I., Papada, L., & Kaliampakos, D. (2022). Energy poverty signs in mountainous Greek areas: The case of Agrafa. International Journal of Sustainable Energy, 41(10), 1408–1433. https://doi.org/10.1080/14786451.2022.2055029
Katsoulakos, N. M., & Kaliampakos, D. C. (2016). Mountainous areas and decentralized energy planning: Insights from Greece. Energy Policy, 91, 174–188. https://doi.org/10.1016/j.enpol.2016.01.007
Kostakis, I. (2020). Socio-demographic determinants of household electricity consumption: Evidence from Greece using quantile regression analysis. Current Research in Environmental Sustainability, 1, 23–30. https://doi.org/10.1016/j.crsust.2020.04.001
Kotsila, D., & Polychronidou, P. (2021). Determinants of household electricity consumption in Greece: A statistical analysis. Journal of Innovation and Entrepreneurship, 10, 19. https://doi.org/10.1186/s13731-021-00161-9
Kugiumtzis, D. (1999). Test your surrogate data before you test for nonlinearity. Physical Review E, 60(3), 2808. https://doi.org/10.1103/PhysRevE.60.2808
Lenzen, M., Wier, M., Cohen, C., Hayami, H., Pachauri, S., & Schaeffer, R. (2006). A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark. India and Japan. Energy, 31(2–3), 181–207. https://doi.org/10.1016/j.energy.2005.01.009
Lepot, M., Aubin, J. B., & Clemens, F. H. (2017). Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment. Water, 9(10), 796. https://doi.org/10.3390/w9100796
Li, X., Smyth, R., Xin, G., & Yao, Y. (2023). Warmer temperatures and energy poverty: Evidence from Chinese households. Energy Economics, 120, 106575. https://doi.org/10.2139/ssrn.4166337
Ma, C., Zhang, Y., & Zhao, W. (2021). Influence of latitude on raw material consumption by biomass combined heat and power plants: Energy conservation study of 50 cities and counties in the cold region of China. Journal of Cleaner Production, 278, 123796. https://doi.org/10.1016/j.jclepro.2020.123796
MacKinnon, J. G. (1992). Model specification tests and artificial regressions. Journal of Economic Literature, 30(1), 102–146.
Matzarakis, A. & Balafoutis, Ch. (2002). Geographical distribution of Heating Degree Days in Greece for Use in Energy Calculations. 6th Pan-hellenic Conference of Meteorology, Climatology and Atmospheric Physics (pp. 156–163). Ioannina, B.D. Katsoulis. (in Greek) https://doi.org/10.1002/joc.1107
McLoughlin, F., Duffy, A., & Conlon, M. (2012). Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study. Energy and Buildings, 48, 240–248. https://doi.org/10.1016/j.enbuild.2012.01.037
Meier, H., & Rehdanz, K. (2010). Determinants of residential space heating expenditures in Great Britain. Energy Economics, 32(5), 949–959. https://doi.org/10.1016/j.eneco.2009.11.008
Moschou, Ch. (2011). Calculation of energy loads for buildings’ energy design using mathematical programming. BSc Thesis. Athens, National Technical University of Athens, School of Chemical Engineering. (In Greek)
Nakagami, H., Murakoshi, C., & Iwafune, Y. (2008). International comparison of household energy consumption and its indicator. Proceedings of the 2008 ACEEE Summer Study on Energy Efficiency in Buildings, 8, 214–224.
Nugaliyadde, A., Somaratne, U., & Wong, K. W. (2019). Predicting electricity consumption using deep recurrent neural networks. arXiv preprint arXiv:1909.08182. https://doi.org/10.48550/arXiv.1909.08182
Ota, T., Kakinaka, M., & Kotani, K. (2018). Demographic effects on residential electricity and city gas consumption in the aging society of Japan. Energy Policy, 115, 503–513. https://doi.org/10.1016/j.enpol.2018.01.016
Papada, L., & Kaliampakos, D. (2016). Developing the energy profile of mountainous areas. Energy, 107, 205–214. https://doi.org/10.1016/j.energy.2016.04.011
Papada, L., & Kaliampakos, D. (2016). Measuring energy poverty in Greece. Energy Policy, 94, 157–165. https://doi.org/10.1016/j.enpol.2016.04.004
Peklaris V. (2010). The crisis creates woodcutters [in Greek]. Agelioforos Newspaper 28 December 2010. http://www.agelioforos.gr/default.asp?pid=7&ct=1&artid=73039. Accessed 10 Dec 2023.
Pérez, N. F., Ferré, J., & Boqué, R. (2009). Calculation of the reliability of classification in discriminant partial least-squares binary classification. Chemometrics and Intelligent Laboratory Systems, 95(2), 122–128. https://doi.org/10.1016/j.chemolab.2008.09.005
Petri, Y., & Caldeira, K. (2015). Impacts of global warming on residential heating and cooling degree-days in the United States. Scientific Reports, 5(1), 12427. https://doi.org/10.1038/srep12427
Quan, S. J., & Li, C. (2021). Urban form and building energy use: A systematic review of measures, mechanisms, and methodologies. Renewable and Sustainable Energy Reviews, 139, 110662. https://doi.org/10.1016/j.rser.2020.110662
Roberts, D., Vera-Toscano, E., & Phimister, E. (2015). Fuel poverty in the UK: Is there a difference between rural and urban areas? Energy Policy, 87, 216–223. https://doi.org/10.1016/j.enpol.2015.08.034
Santamouris, M., Kapsis, K., Korres, D., Livada, I., Pavlou, C., & Assimakopoulos, M. N. (2007). On the relation between the energy and social characteristics of the residential sector. Energy and Buildings, 39(8), 893–905. https://doi.org/10.1016/j.enbuild.2006.11.001
Santamouris, M., Paravantis, J. A., Founda, D., Kolokotsa, D., Michalakakou, P., Papadopoulos, A. M., ... & Servou, E. (2013). Financial crisis and energy consumption: A household survey in Greece. Energy and Buildings, 65, 477–487. https://doi.org/10.1016/j.enbuild.2013.06.024
Sardianou, E. (2007). Estimating energy conservation patterns of Greek households. Energy Policy, 35(7), 3778–3791. https://doi.org/10.1016/j.enpol.2007.01.020
Sardianou, E. (2008). Estimating space heating determinants: An analysis of Greek households. Energy and Buildings, 40(6), 1084–1093. https://doi.org/10.1016/j.enbuild.2007.10.003
Schuler, M., Stucki, E., Roque, O., & Perlik, M. (2004). Mountain Areas in Europe: Analysis of mountain areas in EU member states, acceding and other European countries
Su, Y. W. (2019). Residential electricity demand in Taiwan: Consumption behavior and rebound effect. Energy Policy, 124, 36–45. https://doi.org/10.1016/j.enpol.2018.09.009
United nations, Sustainable development goals, Available at https://unric.org/el/17-%CF%83%CF%84%CE%BF%CF%87%CE%BF%CE%B9-%CE%B2%CE%B9%CF%89%CF%83%CE%B9%CE%BC%CE%B7%CF%83-%CE%B1%CE%BD%CE%B1%CF%80%CF%84%CF%85%CE%BE%CE%B7%CF%83/ (Accessed: 24/4/2023)
Vogiatzi, C., Gemenetzi, G., Massou, L., Poulopoulos, S., Papaefthimiou, S., & Zervas, E. (2018). Energy use and saving in residential sector and occupant behavior: A case study in Athens. Energy and Buildings, 181, 1–9. https://doi.org/10.1016/j.enbuild.2018.09.039
Wang, Z., Bui, Q., Zhang, B., Nawarathna, C. L. K., & Mombeuil, C. (2021). The nexus between renewable energy consumption and human development in BRICS countries: The moderating role of public debt. Renewable Energy, 165, 381–390. https://doi.org/10.1016/j.renene.2020.10.144
Wiesmann, D., Lima Azevedo, I., Ferrão, P., & Fernández, J. E. (2011). Residential electricity consumption in Portugal: Findings from top-down and bottom-up models. Energy Policy, 39(5), 2772–2779. https://doi.org/10.1016/j.enpol.2011.02.047
Ye, Y., Koch, S. F., & Zhang, J. (2018). Determinants of household electricity consumption in South Africa. Energy economics, 75, 120–133. https://doi.org/10.1016/j.eneco.2018.08.005
Zhang, Y., Liu, Q., & Song, L. (2018). Sentence-state LSTM for text representation. arXiv preprint arXiv:1805.02474. https://doi.org/10.48550/arXiv.1805.02474
Zhao, J., Thinh, N. X., & Li, C. (2017). Investigation of the impacts of urban land use patterns on energy consumption in China: a case study of 20 provincial capital cities. Sustainability, 9(8), 1383. https://doi.org/10.3390/su9081383
Zhou, S., & Teng, F. (2013). Estimation of urban residential electricity demand in China using household survey data. Energy Policy, 61, 394–402. https://doi.org/10.1016/j.enpol.2013.06.092
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Balaskas, A., Karani, I., Katsoulakos, N. et al. Understanding the energy behavior of households in the mountainous town of Metsovo, Greece. Energy Efficiency 17, 77 (2024). https://doi.org/10.1007/s12053-024-10258-1
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DOI: https://doi.org/10.1007/s12053-024-10258-1